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See detailA guide for building biological pathways along with two case studies: hair and breast development.
Trindade, Daniel; Orsine, Lissur A.; Barbosa Da Silva, Adriano UL et al

in Methods (2015), 74

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical ... [more ▼]

Genomic information is being underlined in the format of biological pathways. Building these biological pathways is an ongoing demand and benefits from methods for extracting information from biomedical literature with the aid of text-mining tools. Here we hopefully guide you in the attempt of building a customized pathway or chart representation of a system. Our manual is based on a group of software designed to look at biointeractions in a set of abstracts retrieved from PubMed. However, they aim to support the work of someone with biological background, who does not need to be an expert on the subject and will play the role of manual curator while designing the representation of the system, the pathway. We therefore illustrate with two challenging case studies: hair and breast development. They were chosen for focusing on recent acquisitions of human evolution. We produced sub-pathways for each study, representing different phases of development. Differently from most charts present in current databases, we present detailed descriptions, which will additionally guide PESCADOR users along the process. The implementation as a web interface makes PESCADOR a unique tool for guiding the user along the biointeractions, which will constitute a novel pathway. [less ▲]

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See detailDISEASES: text mining and data integration of disease-gene associations.
Pletscher-Frankild, Sune; Palleja, Albert; Tsafou, Kalliopi et al

in Methods (2015), 74

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The ... [more ▼]

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. [less ▲]

Detailed reference viewed: 131 (6 UL)