{"title":"Transforming OMIC features for classification using siamese convolutional networks.","authors":"Qian Wang, Meiyu Duan, Yusi Fan, Shuai Liu, Yanjiao Ren, Lan Huang, Fengfeng Zhou","doi":"10.1142/S0219720022500135","DOIUrl":null,"url":null,"abstract":"<p><p>Modern biotechnologies have generated huge amount of OMIC data, among which transcriptomes and methylomes are two major OMIC types. Transcriptomes measure the expression levels of all the transcripts while methylomes depict the cytosine methylation levels across a genome. Both OMIC data types could be generated by array or sequencing. And some studies deliver many more features (the number of features is denoted as [Formula: see text]) for a sample than the number [Formula: see text] of samples in a cohort, which induce the \"large [Formula: see text] small [Formula: see text]\" paradigm. This study focused on the classification problem about OMIC with \"large [Formula: see text] small [Formula: see text]\" paradigm. A Siamese convolutional network was utilized to transform the OMIC features into a new space with minimized intra-class distances and maximized inter-class distances between the samples. The proposed feature engineering algorithm SiaCo was comprehensively evaluated using both transcriptome and methylome datasets. The experimental data showed that SiaCo generated SiaCo features with improved classification accuracies for binary classification problems, and achieved improvements on the independent test dataset. The individual SiaCo features did not show better inter-class discrimination powers than the original OMIC features. This may be due to that the Siamese convolutional network optimized the collective performances of the SiaCo features, instead of the individual feature's discrimination power. The inherent transformation nature of the Siamese twin network also makes the SiaCo features lack of interpretability. The source code of SiaCo is freely available at http://www.healthinformaticslab.org/supp/resources.php.</p>","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bioinformatics and Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1142/S0219720022500135","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/9 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Modern biotechnologies have generated huge amount of OMIC data, among which transcriptomes and methylomes are two major OMIC types. Transcriptomes measure the expression levels of all the transcripts while methylomes depict the cytosine methylation levels across a genome. Both OMIC data types could be generated by array or sequencing. And some studies deliver many more features (the number of features is denoted as [Formula: see text]) for a sample than the number [Formula: see text] of samples in a cohort, which induce the "large [Formula: see text] small [Formula: see text]" paradigm. This study focused on the classification problem about OMIC with "large [Formula: see text] small [Formula: see text]" paradigm. A Siamese convolutional network was utilized to transform the OMIC features into a new space with minimized intra-class distances and maximized inter-class distances between the samples. The proposed feature engineering algorithm SiaCo was comprehensively evaluated using both transcriptome and methylome datasets. The experimental data showed that SiaCo generated SiaCo features with improved classification accuracies for binary classification problems, and achieved improvements on the independent test dataset. The individual SiaCo features did not show better inter-class discrimination powers than the original OMIC features. This may be due to that the Siamese convolutional network optimized the collective performances of the SiaCo features, instead of the individual feature's discrimination power. The inherent transformation nature of the Siamese twin network also makes the SiaCo features lack of interpretability. The source code of SiaCo is freely available at http://www.healthinformaticslab.org/supp/resources.php.
期刊介绍:
The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information.
The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.