A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data-Reference-Cited by-同舟云学术

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A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data

Published:2021-05 Issue: Volume:189 Page:54-64
ISSN:1046-2023
Container-title:Methods
language:en
Short-container-title:Methods

Author:

Wang Dingjie,Zou Xiufen,Fai Au Kin

Funder

Ohio State University

National Key Research and Development Program of China Stem Cell and Translational Research

University of Iowa

Foundation for the National Institutes of Health

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology,Molecular Biology

Reference45 articles.

1. Assessment of network module identification across complex diseases;Choobdar;Nat. Methods,2019

2. A. Pratapa, A.P. Jalihal, J.N. Law, A. Bharadwaj, T. Murali, “Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data,” Nature Methods, pp. 1-8, 2020.

3. S. van Dam, U. Võsa, A. van der Graaf, L. Franke, and J. P. de Magalhães, “Gene co-expression analysis for functional classification and gene–disease predictions,” Briefings in bioinformatics, p. bbw139, 2017.

4. S. Jin, D. Wang, X. Zou, “Trajectory control in nonlinear networked systems and its applications to complex biological systems,” SIAM Journal on Applied Mathematics, vol. 78, no. 1, pp. 629-649, 2018.

5. Coming of age: ten years of next-generation sequencing technologies;Goodwin;Nat. Rev. Genet.,2016

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1. Machine learning for the analysis of multi-omics data;Methods;2021-05

2. A general index for linear and nonlinear correlations for high dimensional genomic data;BMC Genomics;2020-11-30

3. Automatic Gene Function Prediction in the 2020’s;Genes;2020-10-27

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