Literature DB >> 25449898

A guide for building biological pathways along with two case studies: hair and breast development.

Daniel Trindade1, Lissur A Orsine2, Adriano Barbosa-Silva3, Elisa R Donnard4, J Miguel Ortega5.   

Abstract

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.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast development; Hair development; PESCADOR; Pathway; PubMed

Mesh:

Year:  2014        PMID: 25449898     DOI: 10.1016/j.ymeth.2014.10.006

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  4 in total

1.  Evolutionary hallmarks of the human proteome: chasing the age and coregulation of protein-coding genes.

Authors:  Katia de Paiva Lopes; Francisco José Campos-Laborie; Ricardo Assunção Vialle; José Miguel Ortega; Javier De Las Rivas
Journal:  BMC Genomics       Date:  2016-10-25       Impact factor: 3.969

2.  The research on gene-disease association based on text-mining of PubMed.

Authors:  Jie Zhou; Bo-Quan Fu
Journal:  BMC Bioinformatics       Date:  2018-02-07       Impact factor: 3.169

3.  Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.

Authors:  Guocai Chen; Yuxi Jia; Lisha Zhu; Ping Li; Lin Zhang; Cui Tao; W Jim Zheng
Journal:  BMC Med Inform Decis Mak       Date:  2019-01-31       Impact factor: 2.796

4.  LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks.

Authors:  Bruna Piereck; Marx Oliveira-Lima; Ana Maria Benko-Iseppon; Sarah Diehl; Reinhard Schneider; Ana Christina Brasileiro-Vidal; Adriano Barbosa-Silva
Journal:  BMC Bioinformatics       Date:  2020-08-24       Impact factor: 3.169

  4 in total

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