Fig 1.
Workflow implemented for data-driven analysis of biomedical literature associating culinary spices and herbs to diseases.
Starting with compilation of an exhaustive dictionary of culinary spices and herbs, towards identification of spice-disease associations, one thread of investigation involved implementation of a computational protocol for text mining of biomedical literature including named entity recognition of herbs/spices as well as diseases, pre-processing, extraction of candidate sentences, manual annotations followed by predictions of associations with a machine learning based model. The other thread involved identification of bioactive spice phytochemicals and linking them to diseases. By integrating tripartite information of spices-phytochemicals-diseases, this study establishes the broad-spectrum benevolence of spices, suggests ways for their disease-specific culinary recommendations and probes potential molecular mechanisms underlying their therapeutic properties. Thus it provides a systems perspective to health effects of spices with potential culinary and medicinal applications.
Table 1.
The class-wise performance metrics for the best CNN model, implementing word, position, part of speech and chunk embedding features, used for spice-disease relationship extraction.
All negative associations were cleaned manually.
Fig 2.
Statistics of spice-disease associations.
Historical trend in biomedical literature reporting spice-disease associations. There is an exponential increase in articles reporting the therapeutic effects of spices in last few decades. Data of research articles archived in MEDLINE till July 2017 is represented in the illustration.
Fig 3.
Statistics of positive and negative disease associations for the top 50 spices with most number of associations.
Notice that certain spices like liquorice (Glycyrrhiza glabra) and celery (Apium graveolens) had equal number of positive as well as negative associations. The bias in number of associations may also indicate the inherent biases in scientific literature suggesting that certain spices are studied more than others.
Fig 4.
Top diseases (Third level of MeSH hierarchy) ranked according to their total number of positive associations.
Numbers shown against the bars indicate the ‘number of spices’ involved in the associations. The number of positive disease associations for spices outnumber the number of negative associations (Fig 5) indicating that spices, in general, have been reported with beneficial health effects.
Fig 5.
Top diseases (Third level of MeSH hierarchy) ranked according to their total number of negative associations.
The numbers mentioned on the bars indicate the number of spices associated negatively with each disease.
Fig 6.
Disease categories (First level of MeSH hierarchy) ranked according to the number of positive associations with spices.
Numbers shown against the bars indicate the ‘number of spices’ linked with each of the associations. The number of positive disease category associations for spices outnumber those with negative associations (Fig 7) further confirming the benevolent health effects of spices.
Fig 7.
Disease categories (First level of MeSH hierarchy) ranked according to the number of negative associations with spices.
Numbers shown against the bars indicate the ‘number of spices’ linked with each of the associations.
Fig 8.
Spices ranked according to their ‘relative benevolence score’ highlighting their broad-spectrum benevolence.
This score enumerates the relative health benefits as reflected in the difference between ‘benevolence spectrum’ and ‘adverse spectrum’ scores. Barring two, all spices had positive scores with a large number of them showing significantly larger therapeutic effects compared to their adverse effects.
Fig 9.
Architecture of the Convolutional Neural Network.
Illustration of the convolutional neural network model utilizing word, position, part of speech and chunk embeddings.