Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification.
Inferring Gene Regulatory Networks from RNA-seq Data Using Kernel Classification.
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了解基因之间的关系对于确定生物体的遗传程序至关重要。这项研究可能会对这种理解产生潜在的影响。本研究旨在通过识别相互作用的基因，构建酵母基因组的遗传网络。这是通过结合不同的数学方法和分类技术来预测两个基因之间的关系来实现的。在识别网络交互的几个测试中实现了高精度。我们的发现为酵母基因组网络中基因之间的潜在相互作用提供了新的见解。我们的结果与该领域高度相关，因为它们强调了分类的力量及其在提高当前对酵母基因组网络的理解中的作用。基因表达谱分析是基因调控网络（GRN）中推断基因调控因子及其潜在靶点最受认可的技术之一。本研究的目的是结合RNA-seq和微阵列数据，以广泛的实验条件为代表，构建出芽酵母Saccharomyces cerevisiae基因组的调控网络。我们引入了数据分析、数据准备和训练模型的管道。几种核分类模型；包括一类、二类和罕见事件分类方法，用于对基因进行分类。我们测试了归一化技术对RNA-seq整体性能的影响。我们的发现为酵母调控网络中基因之间的相互作用提供了新的见解。我们的研究结论具有重要意义，因为它们突出了分类的有效性及其对增强酵母调控网络的理解的贡献。经过评估，我们的管道在不同的统计指标上表现出色，例如99%的召回率和98%的AUC得分。
Understanding the relationships between genes is crucial to identify the genetic program of organisms. This research could potentially have implications for this understanding. This study aims to build a genetic network for the yeast genome by recognizing interacting genes. This is performed by incorporating different mathematical methodologies and classification techniques to predict relations between two genes. High accuracy is achieved across several tests to identify network interactions. Our findings provide new insights and potential interactions between genes in the yeast genome network. Our results are highly relevant to the field, as they emphasize the power of classification and its role in improving the current understanding of the yeast genome network. Gene expression profiling is one of the most recognized techniques for inferring gene regulators and their potential targets in gene regulatory networks (GRN). The purpose of this study is to build a regulatory network for the budding yeast Saccharomyces cerevisiae genome by incorporating the use of RNA-seq and microarray data represented by a wide range of experimental conditions. We introduce a pipeline for data analysis, data preparation, and training models. Several kernel classification models; including one-class, two-class, and rare event classification methods, are used to categorize genes. We test the impact of the normalization techniques on the overall performance of RNA-seq. Our findings provide new insights into the interactions between genes in the yeast regulatory network. The conclusions of our study have significant importance since they highlight the effectiveness of classification and its contribution towards enhancing the present comprehension of the yeast regulatory network. When assessed, our pipeline demonstrates strong performance across different statistical metrics, such as a 99% recall rate and a 98% AUC score.
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使用核 Logistic 回归推断酵母基因关联网络中的因果关系。
DOI：
10.1177/1176934320920310
发表时间：
2020
期刊：
Evolutionary bioinformatics online
影响因子：
--
作者：
Al-Aamri A;Taha K;Maalouf M;Kudlicki A;Homouz D
通讯作者：
Homouz D
影响因子：
14.9
作者：
Koltai H;Weingarten-Baror C
通讯作者：
Weingarten-Baror C
影响因子：
48
作者：
Marbach, Daniel;Costello, James C.;Kueffner, Robert;Vega, Nicole M.;Prill, Robert J.;Camacho, Diogo M.;Allison, Kyle R.;Kellis, Manolis;Collins, James J.;Stolovitzky, Gustavo
通讯作者：
Stolovitzky, Gustavo
影响因子：
4
作者：
Razaghi-Moghadam, Zahra;Nikoloski, Zoran
通讯作者：
Nikoloski, Zoran
DOI：
10.1093/database/bau014
发表时间：
2014
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Yang TH;Wang CC;Wang YC;Wu WS
通讯作者：
Wu WS