Abstract
To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard ‘Bag-of-Words’. Several combinations of these genetic based feature sets outperformed ‘Bag-of-Words’ at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as ‘considered’. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.
Highlights
To better understand the genetic mechanisms of complex diseases, we generated a manageable set of biologically validated genes that incorporates the elements of the discovery in genome-wide investigations
We found no significant differences in the Area Under the Receiver Operator Characteristic (AUROC) curves for all methods addressing class imbalances for Database for Preeclampsia (dbPEC)
Training and testing on Database for Preterm birth (dbPTB) showed a significant increase in the AUROC of the Neural Network classifier between models with class imbalances addressed by weights when compared to oversampling shown in Supplementary Table S2 and Supplementary Table S3
Summary
To better understand the genetic mechanisms of complex diseases, we generated a manageable set of biologically validated genes that incorporates the elements of the discovery in genome-wide investigations. We used web-based semantic data mining of published literature to recover articles that contained genes or genetic variants potentially related to diseases of interest. To add a discovery-based approach to our strategy, we screened publicly available, genome-wide databases for additional information. Curators read each article and identified the genes supported by experimentally validated biological relevance for the conditions of interest Using this strategy, we built publicly available databases for two complex reproductive disorders: (i) the Database for Preterm birth (dbPTB) and (ii) The Database for Preeclampsia (dbPEC) [1, 2]
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