TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning
Brief Bioinform. 2023 Mar 23:bbad116. doi: 10.1093/bib/bbad116. Online ahead of print.ABSTRACTMajor histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, lo...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Guangshuai Wang Tao Wu Wei Ning Kaixuan Diao Xiaoqin Sun Jinyu Wang Chenxu Wu Jing Chen Dongliang Xu Xue-Song Liu Source Type: research
MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors
Brief Bioinform. 2023 Mar 23:bbad115. doi: 10.1093/bib/bbad115. Online ahead of print.ABSTRACTMOTIVATION: Histones are the chief protein components of chromatin, and the chemical modifications on histones crucially influence the transcriptional state of related genes. Histone modifying enzyme (HME), responsible for adding or removing the chemical labels, has emerged as a very important class of drug target, with a few HME inhibitors launched as anti-cancerous drugs and tens of molecules under clinical trials. To accelerate the drug discovery process of HME inhibitors, machine learning-based predictive models have been deve...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Qi Lu Ruihan Zhang Hongyuan Zhou Dongxuan Ni Weilie Xiao Jin Li Source Type: research
Analysis of super-enhancer using machine learning and its application to medical biology
Brief Bioinform. 2023 Mar 23:bbad107. doi: 10.1093/bib/bbad107. Online ahead of print.ABSTRACTThe analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medici...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Ryuji Hamamoto Ken Takasawa Norio Shinkai Hidenori Machino Nobuji Kouno Ken Asada Masaaki Komatsu Syuzo Kaneko Source Type: research
Benchmark of embedding-based methods for accurate and transferable prediction of drug response
In this study, we focus on and evaluate deep learning methods using transcriptome data for the long-standing question of personalized drug-response prediction. We developed an embedding-based approach for drug-response prediction and benchmarked similar methods for their performance. For all methods, we used pretreatment transcriptome data to train models and then conducted a comprehensive evaluation and comparison of the models using cross-panels, cross-datasets and target genes. We further validated the methods using three independent datasets assessing multiple compounds for their predictive capability of drug response,...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Peilin Jia Ruifeng Hu Zhongming Zhao Source Type: research
rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations
Brief Bioinform. 2023 Mar 24:bbad108. doi: 10.1093/bib/bbad108. Online ahead of print.ABSTRACTIntra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent represe...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Zhenhua Yu Furui Liu Fangyuan Shi Fang Du Source Type: research
ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures
Brief Bioinform. 2023 Mar 24:bbad119. doi: 10.1093/bib/bbad119. Online ahead of print.ABSTRACTExosomes cargo tumour-characterized biomolecules secreted from cancer cells and play a pivotal role in tumorigenesis and cancer progression, thus providing their potential for non-invasive cancer monitoring. Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose 'ExosomePuri...
Source: Briefings in Bioinformatics - March 24, 2023 Category: Bioinformatics Authors: Tao Wu Yao Dai Yue Xu Jie Zheng Shuting Chen Yinuo Zhang Peng Tian Xiaoqi Zheng Haiyun Wang Source Type: research
A computational approach for the identification of distant homologs of bacterial riboswitches based on inverse RNA folding
Brief Bioinform. 2023 Mar 23:bbad110. doi: 10.1093/bib/bbad110. Online ahead of print.ABSTRACTRiboswitches are conserved structural ribonucleic acid (RNA) sensors that are mainly found to regulate a large number of genes/operons in bacteria. Presently, >50 bacterial riboswitch classes have been discovered, but only the thiamine pyrophosphate riboswitch class is detected in a few eukaryotes like fungi, plants and algae. One of the most important challenges in riboswitch research is to discover existing riboswitch classes in eukaryotes and to understand the evolution of bacterial riboswitches. However, traditional search ...
Source: Briefings in Bioinformatics - March 23, 2023 Category: Bioinformatics Authors: Sumit Mukherjee Matan Drory Retwitzer Sara M Hubbell Michelle M Meyer Danny Barash Source Type: research
A computational approach for the identification of distant homologs of bacterial riboswitches based on inverse RNA folding
Brief Bioinform. 2023 Mar 23:bbad110. doi: 10.1093/bib/bbad110. Online ahead of print.ABSTRACTRiboswitches are conserved structural ribonucleic acid (RNA) sensors that are mainly found to regulate a large number of genes/operons in bacteria. Presently, >50 bacterial riboswitch classes have been discovered, but only the thiamine pyrophosphate riboswitch class is detected in a few eukaryotes like fungi, plants and algae. One of the most important challenges in riboswitch research is to discover existing riboswitch classes in eukaryotes and to understand the evolution of bacterial riboswitches. However, traditional search ...
Source: Briefings in Bioinformatics - March 23, 2023 Category: Bioinformatics Authors: Sumit Mukherjee Matan Drory Retwitzer Sara M Hubbell Michelle M Meyer Danny Barash Source Type: research
Correction to: Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design
Brief Bioinform. 2023 Mar 22:bbad128. doi: 10.1093/bib/bbad128. Online ahead of print.NO ABSTRACTPMID:36946226 | DOI:10.1093/bib/bbad128 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Source Type: research
Poincar é maps for visualization of large protein families
Brief Bioinform. 2023 Mar 22:bbad103. doi: 10.1093/bib/bbad103. Online ahead of print.ABSTRACTIn the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for v...
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Authors: Anna Klimovskaia Susmelj Yani Ren Yann Vander Meersche Jean-Christophe Gelly Tatiana Galochkina Source Type: research
Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer
Brief Bioinform. 2023 Mar 22:bbad078. doi: 10.1093/bib/bbad078. Online ahead of print.ABSTRACTColorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq...
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Authors: Mengsha Tong Yuxiang Lin Wenxian Yang Jinsheng Song Zheyang Zhang Jiajing Xie Jingyi Tian Shijie Luo Chenyu Liang Jialiang Huang Rongshan Yu Source Type: research
Correction to: Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design
Brief Bioinform. 2023 Mar 22:bbad128. doi: 10.1093/bib/bbad128. Online ahead of print.NO ABSTRACTPMID:36946226 | DOI:10.1093/bib/bbad128 (Source: Briefings in Bioinformatics)
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Source Type: research
Poincar é maps for visualization of large protein families
Brief Bioinform. 2023 Mar 22:bbad103. doi: 10.1093/bib/bbad103. Online ahead of print.ABSTRACTIn the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for v...
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Authors: Anna Klimovskaia Susmelj Yani Ren Yann Vander Meersche Jean-Christophe Gelly Tatiana Galochkina Source Type: research
Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer
Brief Bioinform. 2023 Mar 22:bbad078. doi: 10.1093/bib/bbad078. Online ahead of print.ABSTRACTColorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq...
Source: Briefings in Bioinformatics - March 22, 2023 Category: Bioinformatics Authors: Mengsha Tong Yuxiang Lin Wenxian Yang Jinsheng Song Zheyang Zhang Jiajing Xie Jingyi Tian Shijie Luo Chenyu Liang Jialiang Huang Rongshan Yu Source Type: research
TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms
In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate he...
Source: Briefings in Bioinformatics - March 20, 2023 Category: Bioinformatics Authors: Qikai Niu Hongtao Li Lin Tong Sihong Liu Wenjing Zong Siqi Zhang SiWei Tian Jingai Wang Jun Liu Bing Li Zhong Wang Huamin Zhang Source Type: research
