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Correction: Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models

  • Faezeh Hosseinzadeh,
  • Mansour Ebrahimi,
  • Bahram Goliaei,
  • Narges Shamabadi

Correction: Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models

  • Faezeh Hosseinzadeh, 
  • Mansour Ebrahimi, 
  • Bahram Goliaei, 
  • Narges Shamabadi

The authors of article 'wish to state that due to an unintentional mistake and misunderstanding in relation to the adequate practice when referring to findings from the literature , text from previous publications was used verbatim without quotations in several parts of the article. Although the references to the relevant publications were included, the text should not have been used verbatim and the authors apologize for this.

The overlap in the text relates to the Introduction and Discussion sections of the article, where sentences from previous publications were reproduced, this relates to the following fragments in the text:

'Patients with non-small cell lung tumors (squamous, AC, and large cell) are treated differently from those with small cell tumors, therefore pathological distinction between these two types of lung tumor is very important. The gene expression patterns made possible the sub classification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival. Gene expression analysis thus promises to extend and refine standard pathologic analysis [4].

'Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples. A combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterize the transcriptome of NSCLC [6].

'In recent studies, some classifiers are used for classification of cancer genes or proteins, for example KNN classifier can have some utility for some microarray classification problems, acting on the entire non-dimension reduced dataset. They show that increasing the dimensionality of these sets (considering pairs, triples or four-tuples, rather than individual transcript sequences one by one) can lead to significant improvements with each dimension gained [9].

'In other study, features of proteins expressed in malignant, benign and both cancers were compared using different screening techniques, clustering methods, decision tree models and generalized rule induction (GRI) algorithms to look for patterns of similarity in two benign and malignant breast cancer groups [10]

'implementing a systematic method that predicts cancer involvement of genes by integrating heterogeneous datasets by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes' [12].

'In summary, extensive and detailed support for the idea that gene expression-based classification of tumors will soon become clinically useful for cancer of the lung have provided' [4].

'Molecular classification of NSCLC using an objective quantitative test can be highly accurate and could be translated into a diagnostic platform for broad clinical application' [40].

'These descriptors serve to represent and distinguish proteins or peptides of different structural, functional and interaction profiles by exploring their distinguished features in compositions, correlations, and distributions of the constituent amino acids and their structural and physicochemical properties'.

Each of these paragraphs contains overlap with text from the citation included at the end of each sentence, with the exception of the last paragraph which overlaps with text from the publication below:

BMC Bioinformatics. 2007 Aug 17;8:300.

Efficacy of different protein descriptors in predicting protein functional families.

Ong SA, Lin HH, Chen YZ, Li ZR, Cao Z.

In addition, some of the text under the Methods section overlaps with that from our previous published papers:

PLoS One. 2011;6(8):e23146.

Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: a new vista in engineering enzymes.

Ebrahimi M, Lakizadeh A, Agha-Golzadeh P, Ebrahimie E, Ebrahimi M.

The identified issues have no bearing on the results and conclusions of the study. The authors apologize for the instances of plagiarism noted above.