Literature DB >> 33872966

KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge.

Daniel Castillo-Secilla1, Juan Manuel Gálvez2, Francisco Carrillo-Perez2, Marta Verona-Almeida2, Daniel Redondo-Sánchez3, Francisco Manuel Ortuno4, Luis Javier Herrera2, Ignacio Rojas2.   

Abstract

KnowSeq R/Bioc package is designed as a powerful, scalable and modular software focused on automatizing and assembling renowned bioinformatic tools with new features and functionalities. It comprises a unified environment to perform complex gene expression analyses, covering all the needed processing steps to identify a gene signature for a specific disease to gather understandable knowledge. This process may be initiated from raw files either available at well-known platforms or provided by the users themselves, and in either case coming from different information sources and different Transcriptomic technologies. The pipeline makes use of a set of advanced algorithms, including the adaptation of a novel procedure for the selection of the most representative genes in a given multiclass problem. Similarly, an intelligent system able to classify new patients, providing the user the opportunity to choose one among a number of well-known and widespread classification and feature selection methods in Bioinformatics, is embedded. Furthermore, KnowSeq is engineered to automatically develop a complete and detailed HTML report of the whole process which is also modular and scalable. Biclass breast cancer and multiclass lung cancer study cases were addressed to rigorously assess the usability and efficiency of KnowSeq. The models built by using the Differential Expressed Genes achieved from both experiments reach high classification rates. Furthermore, biological knowledge was extracted in terms of Gene Ontologies, Pathways and related diseases with the aim of helping the expert in the decision-making process. KnowSeq is available at Bioconductor (https://bioconductor.org/packages/KnowSeq), GitHub (https://github.com/CasedUgr/KnowSeq) and Docker (https://hub.docker.com/r/casedugr/knowseq).
Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Bioconductor; Bioinformatics; Classification; Enrichment; Gene expression

Year:  2021        PMID: 33872966     DOI: 10.1016/j.compbiomed.2021.104387

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  2 in total

1.  Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis.

Authors:  Francisco Carrillo-Perez; Juan Carlos Morales; Daniel Castillo-Secilla; Olivier Gevaert; Ignacio Rojas; Luis Javier Herrera
Journal:  J Pers Med       Date:  2022-04-08

2.  MSPJ: Discovering potential biomarkers in small gene expression datasets via ensemble learning.

Authors:  HuaChun Yin; JingXin Tao; Yuyang Peng; Ying Xiong; Bo Li; Song Li; Hui Yang
Journal:  Comput Struct Biotechnol J       Date:  2022-07-14       Impact factor: 6.155

  2 in total

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