Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer
Brief Bioinform. 2024 Jan 22;25(2):bbae071. doi: 10.1093/bib/bbae071.ABSTRACTThe evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven me...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Yitao Lu Qian Chu Zhen Li Mengdi Wang Robert Gatenby Qingpeng Zhang Source Type: research
Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis
Brief Bioinform. 2024 Jan 22;25(2):bbae093. doi: 10.1093/bib/bbae093.ABSTRACTSingle-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel ne...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Jiating Yu Jiacheng Leng Zhichao Hou Duanchen Sun Ling-Yun Wu Source Type: research
The rise of taxon-specific epitope predictors
Brief Bioinform. 2024 Jan 22;25(2):bbae092. doi: 10.1093/bib/bbae092.ABSTRACTComputational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when ...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Felipe Campelo Francisco P Lobo Source Type: research
Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data
Brief Bioinform. 2024 Jan 22;25(2):bbae102. doi: 10.1093/bib/bbae102.ABSTRACTIn recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effec...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Dayu Hu Ke Liang Zhibin Dong Jun Wang Yawei Zhao Kunlun He Source Type: research
scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data
Brief Bioinform. 2024 Jan 22;25(2):bbae101. doi: 10.1093/bib/bbae101.ABSTRACTClustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cel...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Yuxuan Chen Ruiqing Zheng Jin Liu Min Li Source Type: research
Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review
Brief Bioinform. 2024 Jan 22;25(2):bbae098. doi: 10.1093/bib/bbae098.ABSTRACTTranslational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts tre...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Krithika Bhuvaneshwar Yuriy Gusev Source Type: research
FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network
Brief Bioinform. 2024 Jan 22;25(2):bbae097. doi: 10.1093/bib/bbae097.ABSTRACTKinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The asse...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Chengyuan Yang Himansu Kumar Pora Kim Source Type: research
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding
Brief Bioinform. 2024 Jan 22;25(2):bbae096. doi: 10.1093/bib/bbae096.ABSTRACTDynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely acces...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Ziheng Duan Siwei Xu Shushrruth Sai Srinivasan Ahyeon Hwang Che Yu Lee Feng Yue Mark Gerstein Yu Luan Matthew Girgenti Jing Zhang Source Type: research
Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data
Brief Bioinform. 2024 Jan 22;25(2):bbae095. doi: 10.1093/bib/bbae095.ABSTRACTRecent advancements in single-cell sequencing technologies have generated extensive omics data in various modalities and revolutionized cell research, especially in the single-cell RNA and ATAC data. The joint analysis across scRNA-seq data and scATAC-seq data has paved the way to comprehending the cellular heterogeneity and complex cellular regulatory networks. Multi-omics integration is gaining attention as an important step in joint analysis, and the number of computational tools in this field is growing rapidly. In this paper, we benchmarked 1...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Chuxi Xiao Yixin Chen Qiuchen Meng Lei Wei Xuegong Zhang Source Type: research
DeTox: a pipeline for the detection of toxins in venomous organisms
Brief Bioinform. 2024 Jan 22;25(2):bbae094. doi: 10.1093/bib/bbae094.ABSTRACTVenomous organisms have independently evolved the ability to produce toxins 101 times during their evolutionary history, resulting in over 200 000 venomous species. Collectively, these species produce millions of toxins, making them a valuable resource for bioprospecting and understanding the evolutionary mechanisms underlying genetic diversification. RNA-seq is the preferred method for characterizing toxin repertoires, but the analysis of the resulting data remains challenging. While early approaches relied on similarity-based mapping to known to...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Allan Ringeval Sarah Farhat Alexander Fedosov Marco Gerdol Samuele Greco Lou Mary Maria Vittoria Modica Nicolas Puillandre Source Type: research
Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer
Brief Bioinform. 2024 Jan 22;25(2):bbae071. doi: 10.1093/bib/bbae071.ABSTRACTThe evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven me...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Yitao Lu Qian Chu Zhen Li Mengdi Wang Robert Gatenby Qingpeng Zhang Source Type: research
Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis
Brief Bioinform. 2024 Jan 22;25(2):bbae093. doi: 10.1093/bib/bbae093.ABSTRACTSingle-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel ne...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Jiating Yu Jiacheng Leng Zhichao Hou Duanchen Sun Ling-Yun Wu Source Type: research
The rise of taxon-specific epitope predictors
Brief Bioinform. 2024 Jan 22;25(2):bbae092. doi: 10.1093/bib/bbae092.ABSTRACTComputational predictors of immunogenic peptides, or epitopes, are traditionally built based on data from a broad range of pathogens without consideration for taxonomic information. While this approach may be reasonable if one aims to develop one-size-fits-all models, it may be counterproductive if the proteins for which the model is expected to generalize are known to come from a specific subset of phylogenetically related pathogens. There is mounting evidence that, for these cases, taxon-specific models can outperform generalist ones, even when ...
Source: Briefings in Bioinformatics - March 17, 2024 Category: Bioinformatics Authors: Felipe Campelo Francisco P Lobo Source Type: research
Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers
Brief Bioinform. 2024 Jan 22;25(2):bbae083. doi: 10.1093/bib/bbae083.ABSTRACTEnhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer...
Source: Briefings in Bioinformatics - March 15, 2024 Category: Bioinformatics Authors: Yao Zhang Pengyu Zhang Hao Wu Source Type: research
Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections
Brief Bioinform. 2024 Jan 22;25(2):bbae084. doi: 10.1093/bib/bbae084.ABSTRACTB cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (te...
Source: Briefings in Bioinformatics - March 15, 2024 Category: Bioinformatics Authors: Sapir Israeli Yoram Louzoun Source Type: research
