Dataset Information

Dealing with the Data Deluge: Handling the Multitude Of Chemical Biology Data Sources.

ABSTRACT: Over the last 20 years, there has been an explosion in the amount and type of biological and chemical data that has been made publicly available in a variety of online databases. While this means that vast amounts of information can be found online, there is no guarantee that it can be found easily (or at all). A scientist searching for a specific piece of information is faced with a daunting task - many databases have overlapping content, use their own identifiers and, in some cases, have arcane and unintuitive user interfaces. In this overview, a variety of well known data sources for chemical and biological information are highlighted, focusing on those most useful for chemical biology research. The issue of using multiple data sources together and the associated problems such as identifier disambiguation are highlighted. A brief discussion is then provided on Tripod, a recently developed platform that supports the integration of arbitrary data sources, providing users a simple interface to search across a federated collection of resources.

SUBMITTER: Guha R

PROVIDER: S-EPMC4655879 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Dealing with the Data Deluge: Handling the Multitude Of Chemical Biology Data Sources.

Guha Rajarshi R Nguyen Dac-Trung DT Southall Noel N Jadhav Ajit A

Current protocols in chemical biology 20120901

Over the last 20 years, there has been an explosion in the amount and type of biological and chemical data that has been made publicly available in a variety of online databases. While this means that vast amounts of information can be found online, there is no guarantee that it can be found easily (or at all). A scientist searching for a specific piece of information is faced with a daunting task - many databases have overlapping content, use their own identifiers and, in some cases, have arcan ...[more]

PMID: 26609498

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Project description:The available curated data lag behind current biological knowledge contained in the literature. Text mining can assist biologists and curators to locate and access this knowledge, for instance by characterizing the functional profile of publications. Gene Ontology (GO) category assignment in free text already supports various applications, such as powering ontology-based search engines, finding curation-relevant articles (triage) or helping the curator to identify and encode functions. Popular text mining tools for GO classification are based on so called thesaurus-based--or dictionary-based--approaches, which exploit similarities between the input text and GO terms themselves. But their effectiveness remains limited owing to the complex nature of GO terms, which rarely occur in text. In contrast, machine learning approaches exploit similarities between the input text and already curated instances contained in a knowledge base to infer a functional profile. GO Annotations (GOA) and MEDLINE make possible to exploit a growing amount of curated abstracts (97 000 in November 2012) for populating this knowledge base. Our study compares a state-of-the-art thesaurus-based system with a machine learning system (based on a k-Nearest Neighbours algorithm) for the task of proposing a functional profile for unseen MEDLINE abstracts, and shows how resources and performances have evolved. Systems are evaluated on their ability to propose for a given abstract the GO terms (2.8 on average) used for curation in GOA. We show that since 2006, although a massive effort was put into adding synonyms in GO (+300%), our thesaurus-based system effectiveness is rather constant, reaching from 0.28 to 0.31 for Recall at 20 (R20). In contrast, thanks to its knowledge base growth, our machine learning system has steadily improved, reaching from 0.38 in 2006 to 0.56 for R20 in 2012. Integrated in semi-automatic workflows or in fully automatic pipelines, such systems are more and more efficient to provide assistance to biologists. DATABASE URL: http://eagl.unige.ch/GOCat/

Dataset Information

Dealing with the Data Deluge: Handling the Multitude Of Chemical Biology Data Sources.

Publications

Dealing with the Data Deluge: Handling the Multitude Of Chemical Biology Data Sources.

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