Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive

  • Joe L. Webb ,

    Contributed equally to this work with: Joe L. Webb, Simon M. Moe

    Roles Conceptualization, Data curation, Methodology, Writing – original draft

    Affiliation Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, United States of America

  • Simon M. Moe ,

    Contributed equally to this work with: Joe L. Webb, Simon M. Moe

    Roles Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, United States of America

  • Andrew K. Bolstad,

    Roles Conceptualization, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States of America

  • Elizabeth M. McNeill

    Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliations Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, United States of America, Neuroscience Interdepartmental Graduate program, Iowa State University, Ames, IA, United States of America

Identification of conserved transcriptome features between humans and Drosophila in the aging brain utilizing machine learning on combined data from the NIH Sequence Read Archive

  • Joe L. Webb, 
  • Simon M. Moe, 
  • Andrew K. Bolstad, 
  • Elizabeth M. McNeill


Aging is universal, yet characterizing the molecular changes that occur in aging which lead to an increased risk for neurological disease remains a challenging problem. Aging affects the prefrontal cortex (PFC), which governs executive function, learning, and memory. Previous sequencing studies have demonstrated that aging alters gene expression in the PFC, however the extent to which these changes are conserved across species and are meaningful in neurodegeneration is unknown. Identifying conserved, age-related genetic and morphological changes in the brain allows application of the wealth of tools available to study underlying mechanisms in model organisms such as Drosophila melanogaster. RNA sequencing data from human PFC and fly heads were analyzed to determine conserved transcriptome signatures of age. Our analysis revealed that expression of 50 conserved genes can accurately determine age in Drosophila (R2 = 0.85) and humans (R2 = 0.46). These transcriptome signatures were also able to classify Drosophila into three age groups with a mean accuracy of 88% and classify human samples with a mean accuracy of 69%. Overall, this work identifies 50 highly conserved aging-associated genetic changes in the brain that can be further studied in model organisms and demonstrates a novel approach to uncovering genetic changes conserved across species from multi-study public databases.


Employing a comparative approach to study conserved aging phenotypes across multiple species provides a deeper insight into molecular aging signatures than studying aging within a single organism. Since aging is the strongest risk factor for developing neurodegenerative diseases, identification of conserved molecular signatures in the brain will allow for more effective study of mechanisms that may underlie neurodegeneration as well as facilitate the development of novel therapeutic strategies to slow cognitive aging [1].

Age-related cognitive decline varies across individuals. Neuroimaging studies indicate that cognitive decline is related to changes in the structure of the prefrontal cortex (PFC) [1] and altered white matter integrity [2]. The PFC is vulnerable to age-related morphology changes [3], with age-related alterations in the cortex occurring across multiple species [2]. Neuronal transcription plays a key role in regulating cognitive function and neural transmission [4], but the extent to which gene expression in the PFC is conserved across species in aging is relatively unknown. A subset of age-related transcriptome changes in the PFC are known to be conserved between humans and mice [5]. In Drosophila, study of neurodegenerative diseases frequently utilizes both the brain, containing the mushroom body which is necessary for olfactory learning and memory [6], and the complex eye of adult Drosophila [7]. Identifying conserved age-related transcriptome changes across model organisms allows for more efficient mechanistic study and may uncover novel therapeutic pathways for preventing age-related cognitive decline or vulnerability to neurodegeneration.

Here, we demonstrate the utility of previously published, publicly available RNA sequencing datasets from humans and Drosophila to increase statistical power through combining samples across multiple laboratories. This facilitates the identification of conserved aging genes in the human PFC/fly head in a unique approach that circumvents the prohibitive cost of collecting hundreds of cross-species samples in a single laboratory. In this work, we examine transcriptome changes across species to identify conserved genes that are highly correlated with chronological age in both human and fly. Furthermore, we were able to predict genetic network interactions of these genes as well as pathways affected by the subset of conserved genes. From a comparative biology perspective, such conserved genes should play an important role in age-related physiological changes in the PFC and comprise a promising target set for future mechanistic analysis in Drosophila, a model organism with a short lifespan and well-established genetic tools. We identify the target gene set by cross-referencing genes highly correlated with age in humans and Drosophila. Machine learning algorithms validate the association of these genes with age. Similar techniques could be used to identify target orthologs in other model organisms and tissues in future analyses.

Materials and methods

Data selection and acquisition

Available sequencing data was identified using the terms “aging”, “RNA-seq”, “brain transcriptome”, “cortex”, and “heads” in articles on PubMed and in the NIH Sequence Read Archive (SRA) database. All eligible studies were published online before August 2019. The following inclusion criteria were used to select data for the analysis: (1) tissue (fly head, human prefrontal cortex); (3) availability of age data; (2) lack of disease (e.g., traumatic brain injury) or treatment; and (4) Illumina format raw next-generation sequencing files. The following exclusion criteria were also used to select data for this study: (1) lack of neurologically normal samples; (3) incomplete age information or incubation temperature data for fly samples; (2) presence of a treatment such as injections, traumatic brain injury or sham-surgery procedures; and (4) studies including only microarray data. When overlapping data of the same study population was encountered in more than one publication across multiple SRA submissions, only the most complete study was used for our analysis leaving a single occurrence for each biological sample. To compare orthologous genes across species, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; [8], was used to identify orthologs between humans and Drosophila. Publicly available data downloaded from the NIH SRA included multiple Bioprojects outlined in Table 1. Files were downloaded from the SRA using Aspera Connect and reads that were downloaded in SRA format were converted using fastq-dump from the NIH SRA-Toolkit [9]. Data were subdivided into three groups for each species. Young was <30 years in humans and ≤10 days in Drosophila, Middle-aged was between 30–60 years in humans and between 10–29 days in Drosophila, Old was classified as above 60 years in humans and above 30 days in Drosophila. Additional sequencing parameters such as read lengths or study metadata can be found for each study contained within this analysis in the NIH Sequence Read Archive (SRA) according to the Bioproject identifiers within Table 1.

Table 1. Study Characteristics: In this table, all publicly available data that were aggregated for this study are described, along with their Sequence Read Archive bioproject numbers, sample descriptors and average number (#) of reads.

Quality control & adapter trimming

All reads were analyzed with FastQC for quality control. Reads with low quality scores (average quality < 10) were discarded. Adapter sequences were trimmed using BBDUK [26]. Reads that matched known Truseq or Nextera adapter sequences were removed during trimming. Individual study manuscripts and supplemental data were examined to identify if reads were sequenced using a forward or reverse library preparation kit.

Alignment & read quantification

Reference fasta genome files and genome annotation gtf files were downloaded from the Ensemble genome browser and browser. Human reads were aligned to the GRCh38 release 94 of the Ensemble Human Genome, and fly reads were aligned to the DMEL release 25 Flybase genome using the STAR v2.5.2 aligner [27]. Transcripts aligning to specific genes were counted using STAR with the quantMode geneCounts function to map transcripts to each genome. Files containing gene counts for all samples are available on GitHub at:

Algorithm selection

Based on previously published work [28, 29], 13 algorithms were selected for regression and classification. The top performing algorithms using default hyperparameters were then used for all subsequent analysis. All models were assessed using all available transcriptome data.

Regression & classification

To identify correlation with biological age, all data were split into a training set (75%) and a testing set (25%) by randomly selecting samples using the train_test_split module in Scikit-learn. Age association models were fit using Scikit-learn v0.23.2 [30] in Jupyter notebooks v.6.1.4 running Python 3.7.3. XGBoost, Gradient Boosting, ADABoost, Bagging Regressor, Random Forest, Extra Trees Regressor, K-nearest neighbors (KNN), Logistic Regression, Linear Discriminant Analysis (LDA), Naive Bayes, Linear Regression, Huber Regression, and ARD Regression were implemented with their default settings in Scikit-learn. Regression performance was evaluated based on mean absolute error, R2, and median absolute error scores in Scikit-learn. All analyses were conducted in batches of 1000 random sampling tests with replacement to estimate the mean for each model performance metric and a 95% confidence interval for R2. In-depth descriptions of these algorithms can be found in Hastie, et al. [31].

Data normalization approaches

We examined Trimmed Mean of M values (TMM) [32], and the more common Relative Log Expression (RLE) method [33] for RNA sequencing read counts. We found that TMM normalization provided more robust regression and classification results. Histograms of log read counts also suggest that TMM normalization removes study effects better than RLE normalization (see S1 Fig).

Calculation of conserved genes

All genes within species were ranked according to correlation coefficient according to within-species age shown in S1 Table. The top 1000 human aging correlated genes were converted into fly homologs using Diopt version 8.0. Human fly homologs were prioritized according to highest to lowest score within both human ensembl ID and flybase ID and the highest ranked homolog for each gene was kept. This left a one to one ‘best match’ between human ensembl ID and flybase ID. This list was cross-referenced to the top 1000 fly aging correlated genes to find matches between the two lists.

Heatmap construction

Expression data of human prefrontal cortex at young, middle, and old was input to the Next-Generation Clustered Heat map (NG-CHM) version 2.16.0 [34].

Genetic interactome analysis with cytoscape

Cytoscape version 3.8.0 was used to generate a genetic interactome of 50 human ensembl IDs with a medium confidence score [35]. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins)—Protein Query generated a node network that allowed functional enrichment data of all 50 genes to be generated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was queried to identify the functional pathways corresponding to each gene in each species. We report those pathways with a false discovery rate (FDR) corrected significance of p ≤ 0.005. Aging correlated genes were cross-referenced to STRING–disease database [36] using a threshold of 1000 genes annotated as disease associated.

Phylogenetic tree mapping

Cyverse DNA Subway was utilized to map all 50 conserved human genes to 49 Drosophila genes. URL: [37]. Protein sequence similarity of all conserved genes was included. Bootstrapped scores of 100 trials are indicated as numerical representations connecting genes to each other.

Panther analysis

Panther (Protein ANalysis THrough Evolutionary Relationships) Classification System (version 15) was used to categorize molecular and cellular classification of genes of interest [38] ( Gene Ontology (GO) Analysis was used to group genes according to cellular location, function and biological processes.

Data and software availability

The data used in this study are publicly accessible through the National Institutes of Health Sequence Read Archive (SRA) [39]. All Ascension numbers can be found in the supplementary data files, along with BioProject numbers in Table 1. The Jupyter notebook describing the workflow, as well as other scripts to conduct analyses within this manuscript are publicly available on GitHub at:

Ethics approval and consent to participate

All studies contained within this manuscript received ethics approval from their respective IRB ethics committees prior to study initiation. See citations in table one for additional information on each published study. Iowa State University Institutional Review Board does not require approval for the secondary data analysis of anonymous publicly available data used in this work.


Combination of publicly available transcriptome datasets

To obtain a higher analytical power than typically feasible from a single experiment in human prefrontal cortex and Drosophila head, we explored methods and constraints for combining data from the National Institutes of Health Sequence Read Archive. We identified key inclusion and exclusion requirements for the combination of data from this publicly available repository. These factors are described in detail in the methods section. The studies meeting these criteria and used for further analysis are described in Table 1. In total, 289 raw fastq files were downloaded from NCBI and processed. Ninety-four percent of the Drosophila reads and ninety-six percent of the human reads were mapped to their respective reference sequences. In our analysis of the combined data sets, 9,650 genes in Drosophila and 16,879 genes in humans had read values of greater than 10 in at least 50% of the samples.

Predictive algorithms for chronological age regression and classification

For regression analysis, we compared 13 distinct algorithms in the Python Scikit-learn library for predicting human chronological age as shown in Table 2A and 2B.

XGBoost performed best with a mean R2 of 0.62 and a median absolute error of 8.28 years on held out data. The standard gradient boosting regressor through Scikit-learn performed similarly with a mean R2 of 0.61 and a median absolute error of 8.72 years (Table 2A). Within Drosophila, XGBoost performed among the best algorithms, with a mean R2 of 0.89. Linear regression, which has been shown previously to predict age from the transcriptome in peripheral blood [40], performed poorly on our data set of complex heterogeneous tissue samples (Table 2A and 2B). Additional details of human and Drosophila regression results using XGBoost can be found in S2 Table. Given its superior predictive power on this specific data set, XGBoost was selected for downstream analysis.

Comparison of algorithms within Drosophila resulted in several algorithms with similar predictive abilities. Four algorithms had a mean R2 value of 0.89 or greater. Of these, XGBoost had the lowest median absolute error. Due to its strong predictive ability in both human and Drosophila in comparison to our other algorithms, XGBoost was selected for downstream analysis.

Algorithms were also compared for classifying samples. This approach is valuable in data sets where exact age is unknown. In a previous report examining machine learning to classify human age groups, an ensemble classifier using linear discriminant analysis (LDA) most accurately classified human chronological age [41]. In our analysis of human PFC and whole fly head, we found that LDA resulted in a mean accuracy of .85 for fly and .73 for human data, respectively. XGBoost performed better with a mean accuracy of .93 for fly and .80 for human data, respectively (S3 Table). Overall, classification of both human and Drosophila samples was possible with a high degree of accuracy (S2 Fig).

Identification of conserved genomic predictors of chronological age in human and Drosophila

A workflow depicting our approach for conserved gene identification can be found in Fig 1. First, we established that gene expression data in the human PFC and fly head were predictive of chronological age within species. Fig 2A shows a sample regression result for human data. These results were obtained using 75% of the samples to train and the remaining 25% for testing. To eliminate the chance of a “lucky draw,” we repeated the 75/25 split 1000 times to assess average performance. Fig 2B depicts the resulting histogram of R2 values. The mean R2 was 0.61, and the median absolute error was 8.07 years. Fig 2C and 2D shows results for fly data, where a mean R2 of 0.93 and a median absolute error of 0.46 days were obtained. The average accuracy of classifying samples as young, middle-aged, or old was 80% for humans and 93% for flies (S3 Table). We repeated this procedure using the 1000 genes most correlated with aging in humans and the 1000 genes most correlated with aging in flies. Using this technique to reduce the number of features in our models resulted in a slight boost in R2 on the testing data for both species, illustrating the potential for error due to overfitting in machine learning models (Compare Figs 2 and 3).

Fig 1. Workflow of biological age prediction using XGBoost across species.

Depicting selection of genes for both aging involvement and conservation between Human and Drosophila.

Fig 2. Biological age prediction using XGBoost across species.

A) Sample regression analysis using all human genes to predict human age. B) Histogram of R2 values for predicting human age with all available human genes with a mean R2 of 0.61. C) A sample regression analysis with all Drosophila genes to predict Drosophila age. D) Histogram of R2 values for predicting Drosophila age using all available Drosophila genes with a mean R2 of 0.93. All results calculated using XGBoost. Histograms represent averages across 1000 bootstrapped random samplings where the regressor or classifier was trained on 75% of the samples and tested on 25% with values reported as the mean across 1000 iterations. Median AE stands for Median Average Error.

Fig 3. Feature selection using aging correlated genes across species.

A) Sample regression analysis using the top 1000 human aging correlated genes to predict human age. B) Histogram of R2 values using the top 1000 human aging correlated genes to predict human age with a mean R2 of 0.62. C) Sample regression analysis predicting Drosophila age using the top 1000 aging correlated Drosophila genes. D) Histogram of R2 values predicting Drosophila age using the top 1000 Drosophila aging correlated genes with mean R2 of 0.95. All results calculated using XGBoost. All histograms represent averages across 1000 bootstrapped random samplings where the regressor or classifier was trained on 75% of the samples and tested on 25%. All histogram values are reported as the mean across 1000 iterations. Median AE stands for median average error.

After determining that aging correlated genes retain predictive ability within species, we wanted to identify a smaller set of genes that could be validated in future animal studies examining their role in aging. Cross referencing the 1000 genes most important in aging within both human and Drosophila through Diopt, we identified genes shared among both lists. This narrowed our window of conserved genes to 50 which were highly predictive of aging in both humans and flies. Fig 4 illustrates the ability to identify human and Drosophila age based on expression levels of these 50 genes. Fig 4A and 4B depicts the human results, where these 50 genes identified chronological age with a mean R2 of 0.46 and median absolute error of 10.79 years. Fig 4C and 4D depicts the results for Drosophila, where a mean R2 = 0.85 and median absolute error of 0.99 days was obtained. In both cases, we pay a small penalty in regression accuracy for reducing the number of genes from 1000 to 50, however both predictions remain strong (see S2 Table). Similar results hold for classification accuracy (see S3 Table). For example, reducing the number of genes used in the prediction by 95% results in only a 15% drop in human age classification accuracy and a 10% drop in fly classification accuracy.

Fig 4. Intersection of homologous aging-correlated genes.

A) Sample regression analysis of the overlapping 50 aging correlated genes across humans. B) Histogram of R2 values of human age prediction using the conserved 50 aging correlated genes with a mean R2 of 0.46. C) Sample regression analysis of the mean R2 values predicting Drosophila age using the 50 aging correlated genes. D) Histogram of R2 values predicting Drosophila age using the conserved 50 aging correlated genes with a mean R2 of 0.85. All results calculated using XGBoost. All histogram results represent averages across 1000 bootstrapped random samplings where the regressor or classifier was trained on 75% of the samples and tested on 25%. All histogram values are reported as the mean across 1000 iterations. Median AE stands for Median Average Error.

Correlated genes enriched in aging signaling pathways

A heatmap of mean human expression data from the prefrontal cortex vs. age in the 50 highly conserved genes indicates an overall increase in expression from young to old age groups in the 50 highly correlated conserved genes (Fig 5A). Higher expression is indicated in red with lower expression indicated in blue.

Fig 5.

A) A heat map of gene expression in young, middle aged, and old human prefrontal cortex of the 50 conserved genes. Shift of color from blue to red indicates an increase in expression relative to all gene expression in the dataset as indicated B) Interactome representing STRING Network interactions of 50 conserved genes. Networks connected through ‘edge’ lines represented by STRING confidence score. Line thickness indicates interaction confidence scores with greater thickness indicating higher confidence interaction. The table on the right indicates KEGG pathways represented by the STRING network. Colors in nodes represent involvement in pathway, see table. Orange border surrounding a node indicates occurrence within 1000 genes with a reported association of either Parkinson’s or Alzheimer’s disease.

Genetic interactions and enrichment of genes in pathways previously known to play an important role in brain aging are revealed using STRING analysis of the 50 highly conserved correlated genes. Fig 5B represents a genetic interactome in which genes are connected based on a STRING confidence score reflecting functional associations. Functional enrichment with KEGG pathways was referenced to identify pathways associated with the 50 genes. Two key pathways known to play an important role in aging were revealed in this analysis. These included the PI3K-akt (9 genes) and MAPK (6 genes). The enrichment of these signaling pathways was found to be significant and survived FDR multiple testing correction (p ≤ 0.005). We observed a high amount of overlap with the genes represented in these two pathways and other significantly enriched pathways such as cancer and focal adhesion as is indicated by the highly connected group of genes which are found in each of these pathways Fig 5B (color coded circles).

As age is one of the greatest predictors of neurodegeneration, our list of 50 genes identified from PFC and fly head were referenced to a STRING disease database. 1000 Genes with a reported association with either Parkinson’s or Alzheimer’s disease were cross-referenced to the list of 50 conserved genes. Within the 1000 genes associated with Alzheimer’s disease and Parkinson’s disease, a total of 7 genes were also identified in our conserved list. The genes associated with Alzheimer’s disease were Erb-B2 receptor tyrosine kinase 2 (ERBB2), Cystatin B (CSTB), and Caspase 6 (CASP6). Those associated with Parkinson’s disease were DNA damage inducible transcript 4(DDIT4), Bone morphogenic protein 4 (BMP4), Coiled-coil-helix-coiled-coil-helix domain containing 10 (CHCHD10), Dilochyl-phosphate mannosphyltransferase 3 (DPM3) and caspase 6 (CASP6) (See orange outline in Fig 5B).

We utilized the Panther Gene Ontology database to reveal evolutionarily relevant functions of our aging correlated genes. Panther Gene Ontology utilizes phylogenetic information to infer evolutionary function of genes that have yet to be well characterized. Genes highly correlated with aging were mapped to Panther pathways as shown in S3A Fig. Panther analysis of the top 1000 human aging-correlated genes indicated that the most highly represented genetic pathway was Gonadotropin-releasing hormone (P06664) with 10 genes represented. Reducing our input to the 50 conserved human genes, 4 remained in the Gonadotropin pathway (S3B Fig). Although the Gonadotrophin pathway is emerging as a possible therapeutic target in aging and neurodegeneration in Humans [42], The gonadotropin-releasing hormone pathway has not been well characterized in Drosophila.

To elucidate roles of the conserved Gonadotropin-hormone genes in Drosophila, we constructed a phylogenetic tree using our 50 conserved genes of Human and Drosophila. Protein sequences of our conserved genes were input into Cyverse–DNA Subway to generate a full phylogenetic tree. S3C Fig depicts a sub-cluster of genes from our phylogenetic tree. Interestingly, 2 of our genes from the Gonadotropin-releasing hormone pathway appeared to share sequence similarity, including ENSG00000123999 (INHA) and ENSG00000125378 (BMP4), along with its homolog FBgn0000490 (dpp).


In this work, we developed a method for combining datasets across multiple studies and organisms giving us the ability to harness the power of a larger dataset than is typically feasible to obtain in a single lab. We then examined several machine learning algorithms to identify the best model for predicting age via highly conserved transcriptome signatures. Using this method, we analyzed publicly available RNA sequencing data from aging Drosophila and humans to reveal aging-associated genes with greater statistical power than can typically be obtained from a single laboratory. Our transcriptome profiling across species uncovered 50 genes whose expression is strongly correlated with aging in both Drosophila and humans. Twenty-two of these genes had not been previously associated with aging (S4 Table).

Previous work supports a cross-species approach to the discovery of gene function in aging. Zullo et al. [43] demonstrated that longevity in humans is related to cortex transcriptome signatures, where genes underlying neural excitation and synaptic regulation are downregulated during aging. By comparing C. elegans with humans, they demonstrate that neural excitation increases as a function of age and inhibiting the excitation of neurons increases longevity. Other key mechanistic relationships such as NADH dehydrogenase expression and longevity [44] as well as dysfunction of mitochondrial proteins and aging [45] have been shown to be highly conserved between invertebrates and humans.

Important consideration must be taken when examining equivalence of invertebrate age to human age. There are several published reports comparing both mouse [46] and rat age [47, 48] to humans, each of which describe different methods for comparing chronological age across species. Although there is not a well-accepted formula for direct conversion between Drosophila age in days to human age in years [49], Drosophila is a common model of aging as denoted by De Nobrega and colleagues [50]. For our work, we created chronological age group cutoffs as indicated in methods based on a similar age comparison to previously published work [51].

Predicted genetic pathways

Our analysis identified an enrichment of two established key aging-related pathways. The PI3K-Akt signaling pathway was heavily represented in our list of 50 genes with 10 being known players in this signaling pathway. This pathway has previously been shown to indirectly promote mTOR complex 1 and mTOR complex 2 kinases [52, 53] as well as decreased levels of Telomeric repeat-binding factor 1 (TRF1) [54]. TRF1 is part of a telomere protective complex which, if lost, results in increased telomere damage. Of the 10 genes found in the PI3K-Akt pathway, two were present in neurodegenerative disease designated datasets in STRING. ERBB2 has previously been shown to decrease expression in the hippocampus of normal adult mice and humans with increased expression in the hippocampus linked to Alzheimer’s disease [55]. Our study demonstrates an increase of ERBB2 expression in the PFC in normal aging, supporting further study into the role that this gene may be playing in the PFC as a risk factor in Alzheimer’s disease. Abnormal signaling in the PI3K-Akt pathway has been shown to lead to hyperphosphorylation of tau, one of the trademarks of Alzheimer’s disease [52]. One of the functions of the PI3K-Akt pathway is regulation of telomere health [53]. Telomere shortening has been demonstrated as a risk factor of various types of neurodegeneration [56] making ERBB2 an interesting target for further study.

Six of our 50 genes were found to be involved in the MAPK signaling pathway. The MAPK signaling cascade has known involvement in neurodegenerative diseases. Activation of MAPK has been shown to increase oxidative stress, which is a key risk factor in both Alzheimer’s and Parkinson’s diseases [57]. Our data also indicated ERBB2 involved in the MAPK signaling pathway. Proper MAPK signaling is crucial for maintaining homeostasis of cell proliferation and differentiation. Over-activation of MAPK signaling has been linked to neuronal inflammation, neuronal death, autophagy, and general Parkinson’s disease phenotypes [58]. Observing levels of ERBB2 with respect to the PI3k-Akt and MAPK signaling pathway could elucidate its role in neurodegenerative diseases. Mutations of ERBB2 could provide key answers into its role in Alzheimer’s and Parkinson’s diseases.

Given the strong correlation between aging and neurodegeneration, we were interested in identifying genes in our list of 50 highly conserved aging genes found in the human PFC that were known to be associated with neurodegenerative diseases. We referenced our list of 50 highly predictive aging genes with 1000 genes associated with Alzheimer’s and Parkinson’s diseases according to the STRING disease database. Given these genes reported associated in neuronal disease alongside conserved expression changes within aging we would propose further mechanistic studies of these genes as risk factors for neurodegenerative diseases associated with aging. These include Caspase-6 (CASP6) whose proteolytically processed form has been shown to increase in correlation with amyloid beta pathologies in Alzheimer’s disease patients [59]. We also identified DDIT4, a known inhibitor of the mTOR pathway whose specific role in aging has not yet been determined [60]. BMP4 has indicated its key role in neurogenesis of Alzheimer’s disease models through downregulation of neurogenesis in the dentate gyrus [61] but has not been studied in the PFC where our observed increased expression in aging may indicate its ability to play a compensatory role. Similarly, decreased expression of CSTB is correlated with disease where mRNA abundance in the peripheral blood has been shown to be decreased in patients with Parkinson’s disease [62]. DPM3 has also been shown to be downregulated in the entorhinal cortex of patients with Alzheimer’s disease [63]. However, all these genes require further study as to their functional role in the PFC.

In the current study, we focused on the human prefrontal cortex (PFC) and the Drosophila head due to the morphological changes in the PFC that often accompany neurological decline. However, comparison of tissue between human PFC and the Drosophila head should be exercised with caution. Numerous tissues outside of the brain exist within the Drosophila head [7].

Our data analysis validated 28 genes previously associated with aging that are conserved between human and Drosophila. Additionally, we identified 22 genes that do not have a previously characterized role in aging. This provides exciting insight into potential targets to follow-up utilizing the Drosophila model. The gonadotropin-hormone pathway is not well studied in Drosophila, yet our data suggests that a small number of conserved age related genes appear to be involved in this pathway in humans. Further investigation into these genes could provide more understanding for how the gonadotropin pathway and other pathways which have not been well studied in aging can be elucidated in the Drosophila model.

Age prediction with machine learning

Other research groups have applied machine learning techniques to predict age from gene expression and biomarker data [64]. In humans, gene expression data from tissue culture of human dermal fibroblasts has been used to train an ensemble of linear discriminant analysis (LDA) classifiers achieving a median absolute error of 4 years and mean absolute error of 7.7 years [43]. We found that LDA performed much worse on our dataset than boosting algorithms (see Table 2). This may be due to the use of heterogeneous tissue samples from the human PFC containing several cell types (e.g., glia, neurons, astrocytes, oligodendrocytes) in the current study compared to tissue culture of a single cell type. Similarly, linear regression has been used to predict age from gene expression in peripheral blood samples [40] but performed poorly on our dataset. When sufficient training data is available, deep neural networks are an attractive option, as demonstrated in [29], where standard blood biomarkers were used to estimate age. With a sample size of 116 human PFC transcriptome profiles, deep learning approaches were not feasible in the current study. Instead, we sought to use approaches that require significantly less data to train. We found that the XGBoost algorithm created the best predictors of human age given human and Drosophila RNA sequencing data among 13 popular machine learning algorithms, as shown in Table 2. We selected the XGBoost algorithm because it performed well in age prediction with human and fly data; however, several other machine learning algorithms could generate models with very high accuracy and low error, especially in fly.

Due to the nature of data collection in animal experiments, samples are normally collected at a few specific ages, such as 10 days or 25 days. The variability in gene expression data from a single time point is likely much less than the variability across a range of ages (e.g., 5–20 days). As a result, models trained on data from a few time points might generalize poorly to new samples from time points never seen by the algorithm. This may explain some of the performance gap between aging via fluorometrically measured pteridine 6-biopterin [65] and our approach. This limitation of the data did not occur with human samples which display a more continuous span of ages. As is common with the use of secondary data there was some imbalance in the number of samples between age groups in fly (young: 30, middle: 24, old: 54); however, S2 Fig indicates that misclassification rates were similar in the three age groups.

Reproducibility and transparency are important current issues in the field of machine learning [66], where models using different random seeds may produce widely different results from one another, ultimately producing data that look as if they come from completely different distributions [67]. To combat this issue, our models were run across 1000 random permutations of data, and we reported averages and confidence intervals for each result. Additionally, genetic data is highly correlated in nature, which may explain why several classification models incorrectly identified some subjects across several re-samplings, highlighting XGBoost’s resilience to bias from multicollinearity. While multicollinearity may explain part of these results, it is unclear if subjects that were incorrectly classified across multiple random draws displayed inherently different transcriptome expression profiles than other subjects at a similar chronological age, or if these errors were due to the inherent random nature of the model. Future studies examining the predictive utility of neurological transcriptome data should work toward determining why specific samples in an age category display different transcriptome signatures, if any arise.

Several problems are inherent to combining data from publicly available repositories. Performing meta-analysis across multiple RNA sequencing datasets is limited by the lack of normalization standards [68] and limited documentation requirements for submitting data to these repositories. Numerous other studies posted on the NIH SRA contained potentially appropriate data to include in our study but did not include any descriptive metadata, preventing us from evaluating whether inclusion or exclusion criteria were met. Enhancing the available data descriptors for publicly available data would enable widespread use of these sequencing data and remove the systematic bias due to the lack of documentation [68]. Another source of bias in meta-analysis of public data is bias towards males. Nearly all the publicly available samples were male, resulting in less than 10% of the samples used in this analysis being from females. The large, unbalanced distribution of biological sexes may have skewed the data to overrepresent age-related changes occurring primarily in males. This limitation could be addressed by future studies including more samples of both biological sexes and making all samples publicly available.

A key strength of this study was that we achieved a large sample size by combining RNA seq samples across studies to improve the overall statistical power. Additionally, we were able to overcome challenges of using gene expression data with different underlying read count distributions due to differences in sample depth across studies through normalizing read counts using the TMM method. Differences among individual datasets still exist, illustrating the necessity for new statistical approaches to combine RNA sequencing data more effectively across multiple studies generated by different sequencers, RNA extraction protocols, library preparation kits, and experimental design of sequencing.

In this study, we uncovered 50 genes conserved between humans and flies capable of accurately predicting age in both species. Roughly half of these genes have been previously associated with aging. We demonstrate that of our 50 genes, 22 novel genes have not yet been published with respect to aging (see S5 Table). Further study of the novel 22 aging-associated genes may shed light on previously unassociated pathways that are altered during aging processes in the PFC. These results position scientists to delve deeper into the underlying conserved mechanisms of these identified aging genes in the PFC. Manipulating the expression of these conserved aging genes could potentially extend lifespan or enable development of novel anti-aging therapeutic compounds. Our dataset combining publicly available transcriptome data across multiple studies and two species demonstrated successful identification of chronological age using neurological transcriptome signatures. Using machine learning to model aging, we identified novel similarities in aging signatures across humans and Drosophila emphasizing the necessity for additional comparative aging research studies.

Supporting information

S1 Table. Aging correlated genes.

This table depicts the aging correlated genes for humans and flies sorted according to their correlation coefficient.


S2 Table. Regression tables predicting chronological age.

This table depicts the average R2, mean square error, median absolute error and R2 95% confidence interval across 1000 iterations of training/testing predictions. Each row represents a different way to select genetic features for age prediction, where each column represents the metric used for evaluating the effectiveness in predicting aging.


S3 Table. Classification tables for age group prediction.

This table depicts the average accuracy, F1, Precision, and Recall scores for classifying samples into their age groups. The data contained in these tables represents average scores across 1000 iterations of training/testing predictions. Each row represents a different way to select genetic features for age prediction, where each column represents the metric used for evaluating the effectiveness in age group classification.


S4 Table. Top 50 conserved aging predictive genes.

This table describes whether previous reports exist linking these genes to aging or neurodegeneration phenotypes in Human or another model organism.


S5 Table. Novel 22 conserved aging predictive genes.

This table describes previous literature of listed genes, along with references.


S1 Fig. Normalization analysis.

Histograms of log2 of read counts by study indicate improved distribution overlap following normalization. A) Results without normalization applied. B) Results after applying Relative Log Expression (RLE) normalization. C) Results after applying Trimmed Mean of M values (TMM) normalization. Each color corresponds to a different study.


S2 Fig. Biological age prediction using XGBoost across species in classification.

Confusion matrices of average biological age prediction. A) Average age group classification results for human samples using all available data. B) Average age group classification results for Drosophila samples using all available data. C) Average age group classification results for 1000 genes in human most correlated with aging. D) Average age group classification results for 1000 genes more correlated with aging in Drosophila. E) Average age group classification results for 50 conserved and correlated genes applied to predict age in humans. F) Average age group classification results for 50 conserved and correlated genes applied to predict age in Drosophila. All confusion matrixes depict the average of 1000 trials of age prediction.


S3 Fig. Panther pathway analysis and phylogenetic relationship.

Panther pathway analysis of genes implicated in aging. A) Predicted pathways using 1000 genes most associated with aging in human data. Threshold set at 5 genes involved in a pathway. B) Pathway analysis of 50 human genes conserved in fly. C) Phylogenetic tree branch depicting gene sequence homology. Genes included are found in the Gonadotropin-releasing hormone pathway (P06664). Numbers throughout the branches indicate bootstrapped scores out of 100 trails testing for sequence similarity. Higher numbers indicate stronger prediction of phylogenetic relationship.



We would like to thank Dr. Peng Liu for her insightful comments during the planning stages of this project. We thank Dr. Hua Bai for critical reading of the manuscript. We are grateful to the teams that made their data available through the sequence read archive database.


  1. 1. Grady CL, McIntosh AR, Craik FIM. Task-related activity in prefrontal cortex and its relation to recognition memory performance in young and old adults. Neuropsychologia. 2005;43(10):1466–81. pmid:15989937
  2. 2. Morrison JH, Baxter MG. The ageing cortical synapse: Hallmarks and implications for cognitive decline. Vol. 13, Nature Reviews Neuroscience. 2012. p. 240–50. pmid:22395804
  3. 3. Zhuang J, Zhang L, Dai S, Cui L, Guo C, Sloofman L, et al. Comparison of multi-tissue aging between human and mouse. Sci Rep. 2019 Dec 1;9(1). pmid:30996271
  4. 4. West AE, Greenberg ME. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Vol. 3, Cold Spring Harbor Perspectives in Biology. 2011. p. 1–21. pmid:21555405
  5. 5. Loerch PM, Lu T, Dakin KA, Vann JM, Isaacs A, Geula C, et al. Evolution of the aging brain transcriptome and synaptic regulation. PLoS One. 2008 Oct 2;3(10). pmid:18830410
  6. 6. Heisenberg M. Mushroom body memoir: From maps to models. Nat Rev Neurosci. 2003;4(4):266–75. pmid:12671643
  7. 7. Torres-Oliva M, Schneider J, Wiegleb G, Kaufholz F, Posnien N. Dynamic genome wide expression profiling of Drosophila head development reveals a novel role of Hunchback in retinal glia cell development and blood-brain barrier integrity. Vol. 14, PLoS Genetics. 2018. pmid:29360820
  8. 8. Hu Y, Flockhart I, Vinayagam A, Bergwitz C, Berger B, Perrimon N, et al. An integrative approach to ortholog prediction for disease-focused and other functional studies. BMC Bioinformatics. 2011;12. pmid:21219653
  9. 9. Alnasir J, Shanahan HP. Investigation into the annotation of protocol sequencing steps in the Sequence Read Archive. Gigascience. 2015;4(1). pmid:25653850
  10. 10. Mertens J, Paquola ACM, Ku M, Hatch E, Böhnke L, Ladjevardi S, et al. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell. 2015;17(6):705–18. pmid:26456686
  11. 11. Hwang T, Park CK, Leung AKL, Gao Y, Hyde TM, Kleinman JE, et al. Dynamic regulation of RNA editing in human brain development and disease. Nat Neurosci. 2016;19(8):1093–9. pmid:27348216
  12. 12. Pantazatos S., Huang Y., Rosoklija G., Dwork A., Arango JM V. Whole-transcriptome brain expression and exon-usage profiling in major depression and suicide: evidence for altered glial, endothelial and ATPase activity. Physiol Behav [Internet]. 2017;176(10):139–48. Available from: file:///C:/Users/Carla%0ACarolina/Desktop/Artigos%0Apara%0Aacrescentar%0Ana%0Aqualificação/The%0Aimpact%0Aof%0Abirth%0Aweight%0Aon%0Acardiovascular%0Adisease%0Arisk%0Ain. pmid:27528462
  13. 13. Wright C, Shin JH, Rajpurohit A, Deep-Soboslay A, Collado-Torres L, Brandon NJ, et al. Altered expression of histamine signaling genes in autism spectrum disorder. Transl Psychiatry. 2017;7(5). pmid:28485729
  14. 14. Bozek K, Wei Y, Yan Z, Liu X, Xiong J, Sugimoto M, et al. Exceptional Evolutionary Divergence of Human Muscle and Brain Metabolomes Parallels Human Cognitive and Physical Uniqueness. PLoS Biol. 2014;12(5). pmid:24866127
  15. 15. He Z, Bammann H, Han D, Xie G, Khaitovich P. Conserved expression of lincRNA during human and macaque prefrontal cortex development and maturation. Rna. 2014;20(7):1103–11. pmid:24847104
  16. 16. Labadorf A, Hoss AG, Lagomarsino V, Latourelle JC, Hadzi TC, Bregu J, et al. RNA sequence analysis of human huntington disease brain reveals an extensive increase in inflammatory and developmental gene expression. PLoS One. 2015;10(12):1–21. pmid:26636579
  17. 17. Labadorf A, Choi SH, Myers RH. Evidence for a pan-neurodegenerative disease response in Huntington’s and Parkinson’s disease expression profiles. Front Mol Neurosci. 2018;10(January):1–12. pmid:29375298
  18. 18. Agus F, Crespo D, Myers RH, Labadorf A. The caudate nucleus undergoes dramatic and unique transcriptional changes in human prodromal Huntington’ s disease brain. 2019;1–17.
  19. 19. Birnbaum A, Wu X, Tatar M, Liu N, Bai H. Age-dependent changes in transcription factor FoxO targeting in female Drosophila. Front Genet. 2019;10(MAY):1–12. pmid:31134124
  20. 20. Yang H, Jaime M, Polihronakis M, Kanegawa K, Markow T, Kaneshiro K, et al. Re-annotation of eight Drosophila genomes. Life Sci Alliance. 2018;1(6):1–14. pmid:30599046
  21. 21. Benner L., Castro E., Whitworth C., Venken K., Yang H., Fang J., et al. Drosophila Heterochromatin Stabilization Requires the. 2019;213(November):877–95.
  22. 22. Gill S., Le H., Melkani SP G. Time-restricted feeding attenuates age-related cardiac decline in Drosophila. Science (80-). 2015;347(6227):1265–70. pmid:25766238
  23. 23. Ma Z, Wang H, Cai Y, Wang H, Niu K, Wu X, et al. Epigenetic Drift of H3K27me3 in Aging Links Glycolysis to Healthy Longevity. bioRxiv. 2018. pmid:29809154
  24. 24. Kennerdell JR et al. MiR-34 inhibits polycomb repressive complex 2 to modulate chaperone expression and promote healthy brain aging., Nat Commun, 2018 Oct 10;9(1):4188 pmid:30305625
  25. 25. Kuintzle RC, Chow ES, Westby TN, Gvakharia BO, Giebultowicz JM, Hendrix DA. during aging. Nat Publ Gr. 2017;1–10. pmid:28221375
  26. 26. Bushnell B. BBDuk: Adapter. Quality Trimming and Filtering. httpssourceforgenetprojectsbbmap.
  27. 27. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan;29(1):15–21. pmid:23104886
  28. 28. Fleischer JG, Schulte R, Tsai HH, Tyagi S, Ibarra A, Shokhirev MN, et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biol. 2018 Dec 20;19(1). pmid:30567591
  29. 29. Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY). 2016;8(5):1021–30. pmid:27191382
  30. 30. Pedregosa F, Michel V, Grisel O, Blondel M, Prettenhofer P, Weiss R, et al. Scikit-learn: Machine Learning in Python. [Internet]. Vol. 12, Journal of Machine Learning Research. 2011. Available from:
  31. 31. Hastie T., Tishirani R., J F. The Elements of Statistical Learning. 2009.
  32. 32. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data [Internet]. 2010. Available from:
  33. 33. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010 Oct 27;11(10). pmid:20979621
  34. 34. Ryan MC, Stucky M, Wakefield C, Melott JM, Akbani R, Weinstein JN, et al. Interactive Clustered Heat Map Builder: An easy web-based tool for creating sophisticated clustered heat maps. F1000Research. 2019 Oct 14;8:1750. pmid:32269754
  35. 35. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov;13(11):2498–504. pmid:14597658
  36. 36. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J Proteome Res. 2019 Feb 1;18(2):623–32. pmid:30450911
  37. 37. Merchant N, Lyons E, Goff S, Vaughn M, Ware D, Micklos D, et al. The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences. PLoS Biol. 2016;14(1):1–9. pmid:26752627
  38. 38. Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019 Jan 8;47(D1):D419–26. pmid:30407594
  39. 39. Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2011 Jan;39(SUPPL. 1).
  40. 40. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6. pmid:26490707
  41. 41. Bushnell B, Rood J, Singer E. BBMerge –Accurate paired shotgun read merging via overlap. PLoS One. 2017 Oct 1;12(10). pmid:29073143
  42. 42. Maggi R. Physiology of Gonadotropin-Releasing Hormone (Gnrh): Beyond the Control of Reproductive Functions. MOJ Anat Physiol. 2016 Jul 5;2(5).
  43. 43. Zullo JM, Drake D, Aron L, O’Hern P, Dhamne SC, Davidsohn N, et al. Regulation of lifespan by neural excitation and REST. Nature. 2019;574(7778):359–64. pmid:31619788
  44. 44. Hamilton B, Dong Y, Shindo M, Liu W, Odell I, Ruvkun G, et al. A systematic RNAi screen for longevity genes in C. elegans. Genes Dev. 2005;19(13):1544–55. pmid:15998808
  45. 45. McCarroll SA, Murphy CT, Zou S, Pletcher SD, Chin CS, Jan YN, et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat Genet. 2004;36(2):197–204. pmid:14730301
  46. 46. Dutta S, Sengupta P. Men and mice: Relating their ages. Vol. 152, Life Sciences. Elsevier Inc.; 2016. p. 244–8. pmid:26596563
  47. 47. Agoston D V. How to translate time? The temporal aspect of human and rodent biology. Vol. 8, Frontiers in Neurology. Frontiers Research Foundation; 2017.
  48. 48. Adami Andreollo N, Freitas dos SANTOS E, Rachel ARAÚJO M, Roberto LOPES L. Rat’s age versus human’s age: What is the relationship? Idade do rato versus idade humana: qual é a relação? [Internet]. Vol. 25, Review Article. 2012. Available from:
  49. 49. Kowalski P, Dihel N, Garcia M. Aging Studies in Drosophila. World. 2008;1(2007):283–331.
  50. 50. De Nobrega AK, Lyons L. Aging and the clock: Perspective from flies to humans. 2018.
  51. 51. Haddadi M, Jahromi SR, Sagar BKKC, Patil RK, Shivanandappa T, Ramesh SR. Brain aging, memory impairment and oxidative stress: A study in Drosophila melanogaster. Behav Brain Res. 2014;259:60–9. pmid:24183945
  52. 52. Karar J, Maity A. PI3K/AKT/mTOR Pathway in Angiogenesis. Front Mol Neurosci. 2011;4. pmid:22144946
  53. 53. Xu F, Na L, Li Y, Chen L. Roles of the PI3K/AKT/mTOR signalling pathways in neurodegenerative diseases and tumours. Cell Biosci [Internet]. 2020;10(1):1–12. Available from: pmid:32266056
  54. 54. Méndez-Pertuz M, Martínez P, Blanco-Aparicio C, Gómez-Casero E, Belen Garciá A, Martínez-Torrecuadrada J, et al. Modulation of telomere protection by the PI3K/AKT pathway. Nat Commun. 2017 Dec 1;8(1). pmid:29097657
  55. 55. Wang BJ, Her GM, Hu MK, Chen YW, Tung YT, Wu PY, et al. ErbB2 regulates autophagic flux to modulate the proteostasis of APP-CTFs in Alzheimer’s disease. Proc Natl Acad Sci U S A. 2017 Apr 11;114(15):E3129–38. pmid:28351972
  56. 56. Houck AL, Seddighi S, Driver JA. At the Crossroads Between Neurodegeneration and Cancer: A Review of Overlapping Biology and Its Implications. Curr Aging Sci. 2018;11(2):77–89. pmid:29552989
  57. 57. Kim EK, Choi EJ. Pathological roles of MAPK signaling pathways in human diseases. Vol. 1802, Biochimica et Biophysica Acta—Molecular Basis of Disease. 2010. p. 396–405. pmid:20079433
  58. 58. Bohush A, Niewiadomska G, Filipek A. Role of mitogen activated protein kinase signaling in parkinson’s disease. Int J Mol Sci. 2018;19(10). pmid:30274251
  59. 59. LeBlanc AC. Caspase-6 as a novel early target in the treatment of Alzheimer’s disease. Vol. 37, European Journal of Neuroscience. 2013. p. 2005–18. pmid:23773070
  60. 60. Lautrup S, Lou G, Aman Y, Nilsen H, Tao J, Fang EF. Microglial mitophagy mitigates neuroinflammation in Alzheimer’s disease. Neurochem Int. 2019 Oct 1;129. pmid:31100304
  61. 61. Tang J, Song M, Wang Y, Fan X, Xu H, Bai Y. Noggin and BMP4 co-modulate adult hippocampal neurogenesis in the APPswe/PS1ΔE9 transgenic mouse model of Alzheimer’s disease. Biochem Biophys Res Commun [Internet]. 2009;385(3):341–5. Available from: pmid:19463786
  62. 62. Belotserkovskaya E. V., Suchkova I. O., Borisova E. V., Borovkova N. K., Pavlinova L. I., Patkin EL. Concurrent changes of CSTB and ACAP3 genes expression in symptomatic epilepsy and parkinson’s disease. Международный научно-исследовательский журнал. 2017;2(12–4 (66)):6–8.
  63. 63. Frenkel-Pinter M, Stempler S, Tal-Mazaki S, Losev Y, Singh-Anand A, Escobar-Álvarez D, et al. Altered protein glycosylation predicts Alzheimer’s disease and modulates its pathology in disease model Drosophila. Neurobiol Aging [Internet]. 2017;56:159–71. Available from: pmid:28552182
  64. 64. Komljenovic A, Li H, Sorrentino V, Kutalik Z, Auwerx J, Robinson-Rechavi M. Cross-species functional modules link proteostasis to human normal aging. PLoS Comput Biol. 2019 Jul 1;15(7). pmid:31269015
  65. 65. Robson SKA, Vickers M, Blows MW, Crozier RH. Age determination in individual wild-caught Drosophila serrata using pteridine concentration. J Exp Biol. 2006;209(16):3155–63. pmid:16888063
  66. 66. Islam R, Henderson P, Gomrokchi M, Precup D. Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control. 2017 Aug 10; Available from:
  67. 67. Colas C, Sigaud O, Oudeyer P-Y. How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments. 2018 Jun 21; Available from:
  68. 68. Roca CP, Gomes SIL, Amorim MJB, Scott-Fordsmand JJ. Variation-preserving normalization unveils blind spots in gene expression profiling. Sci Rep. 2017 Mar 9;7. pmid:28276435