Dataset Information

ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains.

ABSTRACT: Many entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Since the PDB now contains many tens of thousands of protein chains, and since protein sequence databases can dwarf such numbers by orders of magnitude, there is a pressing need to develop automatic structure-function annotation tools which can operate at the domain level.This article presents ECDomainMiner, a novel content-based filtering approach to automatically infer associations between EC numbers and Pfam domains. ECDomainMiner finds a total of 20,728 non-redundant EC-Pfam associations with a F-measure of 0.95 with respect to a "Gold Standard" test set extracted from InterPro. Compared to the 1515 manually curated EC-Pfam associations in InterPro, ECDomainMiner infers a 13-fold increase in the number of EC-Pfam associations.These EC-Pfam associations could be used to annotate some 58,722 protein chains in the PDB which currently lack any EC annotation. The ECDomainMiner database is publicly available at http://ecdm.loria.fr/ .

SUBMITTER: Alborzi SZ

PROVIDER: S-EPMC5307852 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains.

Alborzi Seyed Ziaeddin SZ Devignes Marie-Dominique MD Ritchie David W DW

BMC bioinformatics 20170213 1

<h4>Background</h4>Many entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Sinc ...[more]

PMID: 28193156

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Project description:BackgroundThe Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients' outcomes. One of the core criteria for "Meaningful Use" of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting.MethodsIn this study, using sampled clinical documents associated with a cohort of patients who received their primary care at Mayo Clinic, we investigated the associations between problem list and practice setting through natural language processing (NLP) and topic modeling techniques. Specifically, after practice settings and problem lists were normalized, statistical χ2 test, term frequency-inverse document frequency (TF-IDF) and enrichment analysis were used to choose representative concepts for each setting. Then Latent Dirichlet Allocations (LDA) were used to train topic models and predict potential practice settings using similarity metrics based on the problem concepts representative of practice settings. Evaluation was conducted through 5-fold cross validation and Recall@k, Precision@k and F1@k were calculated.ResultsOur method can generate prioritized and meaningful problem lists corresponding to specific practice settings. For practice setting prediction, recall increases from 0.719 (k = 2) to 0.931 (k = 10), precision increases from 0.882 (k = 2) to 0.931 (k = 10) and F1 increases from 0.790 (k = 2) to 0.931 (k = 10).ConclusionTo our best knowledge, our study is the first attempting to discover the association between the problem lists and hospital practice settings. In the future, we plan to investigate how to provide more tailored care by utilizing the association between problem list and practice setting revealed in this study.

Dataset Information

ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains.

Publications

ECDomainMiner: discovering hidden associations between enzyme commission numbers and Pfam domains.

Similar Datasets

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