利用限制性玻尔兹曼机鉴定结直肠癌基因相互作用的程度。

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
IET Systems Biology Pub Date : 2021-02-01 Epub Date: 2020-12-08 DOI:10.1049/syb2.12009
Sujay Saha, Saikat Bandopadhyay, Anupam Ghosh
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引用次数: 0

摘要

两个或两个以上的基因在一个生物体的单一性状的发育过程中以各种方式影响彼此的表达的现象被称为基因相互作用。基因相互作用不仅适用于正常的人类特征,也适用于患病样本。因此,基因相互作用的分析可以帮助我们区分正常和患病样本,或者区分任何患病样本的两个/多个阶段。在这项工作的第一阶段,我们使用了限制性玻尔兹曼机器模型来发现在结直肠癌数据集(GDS4382)的20个基因的每个基因对的正常和/或癌症样本中存在如此显著的相互作用,以及这些相互作用的权重/程度。随后,我们正在寻找结直肠癌数据集(GDS1777)中相同的20个基因的腺瘤和/或癌样本中存在的这些相互作用。这些相互作用的权重/程度代表了相互作用的强弱。最后,我们将在这些相互作用的帮助下创建一个基因调控网络,其中通过使用Naïve贝叶斯分类器识别调控基因。通过与NCBI数据库的相互作用比较,对实验结果进行了生物学验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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