Using binary classification to prioritize and curate articles for the Comparative Toxicogenomics Database

Mechanism Study of Polydatin in Treating Spinal Cord Injury by Modulating Mitochondrial Membrane Potential Based on Network Pharmacology and Molecular Docking

Spinal cord injury (SCI) is one of the most devastating central lesions, and mitochondrial function plays an important role in secondary injury after SCI. Polydatin (PD) is a natural glycosylated precursor of resveratrol, showing mitochondrial preservation effects in the central nervous system. This study aimed to identify the hub target genes of PD on mitochondrial membrane potential (MMP) in SCI. A comprehensive analysis was performed on SCI-related genes, MMP-related genes, and PD targets screening from public databases. Differential expression analysis was conducted to identify differentially expressed genes (DEGs) in SCI. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were employed to assess pathway enrichment. Protein-protein interaction (PPI) network analysis and molecular docking were conducted to identify key genes and evaluate the binding affinity between PD and hub genes. A total of 16,958 SCI-related genes, 2,786 MMP-related genes, 318 PD-related target genes, and 7229 DEGs were identified. Intersection analysis revealed 46 genes common to all four categories. GSEA and GSVA analysis identified significant enrichment of pathways associated with suppressed and activated SCI biological processes. The PPI network analysis identified seven core hub genes: EGFR, SRC, VEGFA, STAT3, ERBB2, TP53, and RHOA. Molecular docking revealed strong binding affinities between PD and ERBB2, EGFR, and RHOA. The findings based on computational investigation from public databases suggest that PD may have therapeutic potential for SCI by modulating MMP. These results contribute to the understanding of SCI pathogenesis and the development of novel therapeutic strategies.

Document triage for identifying protein-protein interactions affected by mutations: a neural network ensemble approach

The precision medicine (PM) initiative promises to identify individualized treatment depending on a patients' genetic profile and their related responses. In order to help health professionals and researchers in the PM endeavor, BioCreative VI organized a PM Track to mine protein-protein interactions (PPI) affected by genetic mutations from the biomedical literature. In this paper, we present a neural network ensemble approach to identify relevant articles describing PPI affected by mutations. In this approach, several neural network models are used for document triage, and the ensemble performs better than any individual model. In the official runs, our best submission achieves an F-score of 69.04% in the BioCreative VI PM document triage task. After post-challenge analysis, to address the problem of the limited size of training set, a PPI pre-trained module is incorporated into our approach to further improve the performance. Finally, our best ensemble method achieves an F-score of 71.04% on the test set.

Information Retrieval and Text Mining Technologies for Chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

Text Mining to Support Gene Ontology Curation and Vice Versa

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

A document classifier for medicinal chemistry publications trained on the ChEMBL corpus

Background

The large increase in the number of scientific publications has fuelled a need for semi- and fully automated text mining approaches in order to assist in the triage process, both for individual scientists and also for larger-scale data extraction and curation into public databases. Here, we introduce a document classifier, which is able to successfully distinguish between publications that are `ChEMBL-like’ (i.e. related to small molecule drug discovery and likely to contain quantitative bioactivity data) and those that are not. The unprecedented size of the medicinal chemistry literature collection, coupled with the advantage of manual curation and mapping to chemistry and biology make the ChEMBL corpus a unique resource for text mining.

Results

The method has been implemented as a data protocol/workflow for both Pipeline Pilot (version 8.5) and KNIME (version 2.9) respectively. Both workflows and models are freely available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/text-mining. These can be readily modified to include additional keyword constraints to further focus searches.

Conclusions

Large-scale machine learning document classification was shown to be very robust and flexible for this particular application, as illustrated in four distinct text-mining-based use cases. The models are readily available on two data workflow platforms, which we believe will allow the majority of the scientific community to apply them to their own data.

Abstract

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-014-0040-8) contains supplementary material, which is available to authorized users.

PubTator: a web-based text mining tool for assisting biocuration

Manually curating knowledge from biomedical literature into structured databases is highly expensive and time-consuming, making it difficult to keep pace with the rapid growth of the literature. There is therefore a pressing need to assist biocuration with automated text mining tools. Here, we describe PubTator, a web-based system for assisting biocuration. PubTator is different from the few existing tools by featuring a PubMed-like interface, which many biocurators find familiar, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results. Through a formal evaluation with two external user groups, PubTator was shown to be capable of improving both the efficiency and accuracy of manual curation. PubTator is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/.