Evidence map›Paper›PMID 33590963›Full record

ArticleIET systems biology2021

Identifying the degree of genetic interactions using Restricted Boltzmann Machine-A study on colorectal cancer.

Sujay Saha, Saikat Bandopadhyay, Anupam Ghosh

Abstract read
In one paragraph

Article in IET systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Sujay SahaDepartment of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, India.
Saikat BandopadhyayDepartment of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, India.
Anupam GhoshDepartment of Computer Science & Engineering, Netaji Subhash Engineering College, Kolkata, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples. At the first stage of this work we have used restricted Boltzmann machine model to find such significant interactions present in normal and/or cancer samples of every gene pairs of 20 genes of colorectal cancer data set (GDS4382) along with the weight/degree of those interactions. Later on, we are looking for those interactions present in adenoma and/or carcinoma samples of the same 20 genes of colorectal cancer data set (GDS1777). The weight/degree of those interactions represents how strong/weak an interaction is. At the end we will create a gene regulatory network with the help of those interactions, where the regulatory genes are identified by using Naïve Bayes Classifier. Experimental results are validated biologically by comparing the interactions with NCBI databases.

Indexed as

Colorectal NeoplasmsGene Regulatory NetworksBayes TheoremHumansMachine Learning

Identifiers

PMID33590963
PMCPMC8675802

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.