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Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM

Chinese Journal of Electronics 2022年第1期31卷 99-106页

作者： DUAN Hua FENG Tong LIU Songning ZHANG Yulin su jionglong College of Mathematics and Systems Science Shandong University of Science and Technology School of AI and Advanced Computing XJTLU Entrepreneur College (Taicang) Xi'an Jiaotong-Liverpool University

A new classification model, the fuzzy hybrid twin support vector machine(TWSVM), namely FHTWSVM, is proposed by combining the fuzzy TWSVM and the hypersphere support vector machine(SVM).The hypersphere SVM is utilized for generating the hyperspheres for the positive and negative class with the smallest possible radius, so that the hyperspheres can contain as many samples as possible. The samples which the hyperspheres cover form a new sample set. Furthermore a distance-based fuzzy function is utilized to calculate the fuzzy factors for the samples. Finally FHTWSVM is used to train all samples with the parameters optimized by grid search. This method can maximize intra-class clustering for noise removal and reduce the influence of *** demonstrate the superiority of the performance of FHTWSVM over other classifiers, e.g., KNN, RF,Bayesian, TWSVM, Ada Boost and XGBoost, a series of experiments is conducted using eight gene expression datasets. The evaluation results show that the proposed approach can improve the classification performance as well as reduce prediction errors for the datasets.

关键词： support vector machines Training Gene expression Speech recognition Mathematical models Tumors Task analysis

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m6A-TSHub:Unveiling the Context-specific m^(6)A Methylation and m^(6)A-affecting Mutations in 23 Human Tissues

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Genomics, Proteomics & Bioinformatics 2023年第4期21卷 678-694页

作者： Bowen Song Daiyun Huang Yuxin Zhang Zhen Wei jionglong su João Pedro de Magalhães Daniel J.Rigden Jia Meng Kunqi Chen Key Laboratory of Gastrointestinal Cancer(Fujian Medical University) Ministry of EducationSchool of Basic Medical SciencesFujian Medical UniversityFuzhou 350004China Department of Mathematical Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Institute of Systems Molecular and Integrative BiologyUniversity of LiverpoolLiverpool L697ZBUnited Kingdom Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Department of Computer Science University of LiverpoolLiverpool L697ZBUnited Kingdom Institute of Ageing&Chronic Disease University of LiverpoolLiverpool L697ZBUnited Kingdom School of AI and Advanced Computing Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China AI University Research Centre Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs(lncRNAs),N6-methyladenosine(m^(6)A)RNA methylation has been shown to participate in essential biological *** studies have revealed the distinct patterns of m^(6)A methylome across human tissues,and a major challenge remains in elucidating the tissue-specific presence and circuitry of m^(6)A *** present here a comprehensive online platform,m^(6)A-TSHub,for unveiling the context-specific m^(6)A methylation and genetic mutations that potentially regulate m^(6)A epigenetic mark.m^(6)A-TSHub consists of four core components,including(1)m^(6)A-TSDB,a comprehensive database of 184,554 functionally annotated m^(6)A sites derived from 23 human tissues and 499,369 m^(6)A sites from 25 tumor conditions,respectively;(2)m^(6)A-TSFinder,a web server for high-accuracy prediction of m^(6)A methylation sites within a specific tissue from RNA sequences,which was constructed using multi-instance deep neural networks with gated attention;(3)m^(6)ATSVar,a web server for assessing the impact of genetic variants on tissue-specific m^(6)A RNA modifications;and(4)m^(6)A-CAVar,a database of 587,983 The Cancer Genome Atlas(TCGA)cancer mutations(derived from 27 cancer types)that were predicted to affect m^(6)A modifications in the primary tissue of *** database should make a useful resource for studying the m^(6)A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue(or cancer type).m^(6)A-TSHub is accessible at ***/biologicalsciences/m^(6)ats.

关键词： N^(6)-methyladenosine Context-specific analysis Cancer mutation Genome analysis Functional annotation

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