Journal of Computational Biology最新文献

Rebuttal to Flaws in the Paper 'Nearly Instantaneous Time-Varying Reproduction Number for Contagious Diseases-a Direct Approach Based on Nonlinear Regression'. 对“传染病近瞬时时变繁殖数——一种基于非线性回归的直接方法”论文缺陷的反驳。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 DOI: 10.1089/cmb.2025.0024

Jūratė Šaltytė Benth, Fred Espen Benth, Espen Rostrup Nakstad

引用次数: 0

Filtering for Highly Variable Genes and High-Quality Spots Improves Phylogenetic Analysis of Cancer Spatial Transcriptomics Visium Data. 高可变基因和高质量位点的过滤改进了癌症空间转录组学的系统发育分析。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-06-11 DOI: 10.1089/cmb.2024.0614

Alexandra Sasha Gavryushkina, Holly R Pinkney, Sarah D Diermeier, Alex Gavryushkin

引用次数: 0

Flaws in the Article "Nearly Instantaneous Time-Varying Reproduction Number for Contagious Diseases-a Direct Approach Based on Nonlinear Regression". “传染病的近瞬时时变繁殖数——一种基于非线性回归的直接方法”一文的缺陷。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 DOI: 10.1177/15578666251360613

Geir Storvik, Solveig Engebretsen, Birgitte Freiesleben de Blasio, Arnoldo Frigessi

引用次数: 0

Special Section: 12th International Computational Advances in Bio and Medical Sciences (ICCABS 2023). 特别部分：第12届国际生物和医学科学计算进展（ICCABS 2023）。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-05-16 DOI: 10.1089/cmb.2025.0124

Mukul S Bansal, Wei Chen, Yury Khudyakov, Ion I Măndoiu, Marmar R Moussa, Murray Patterson, Sanguthevar Rajasekaran, Pavel Skums, Sharma V Thankachan, Alex Zelikovsky

引用次数: 0

An Exact Matching Method for 16S rRNA Taxonomy Classification. 16S rRNA分类分类的精确匹配方法。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-06-09 DOI: 10.1089/cmb.2024.0615

Sing-Hoi Sze

引用次数: 0

An Algorithm to Calculate the p-Value of the Monge-Elkan Distance. 一种计算Monge-Elkan距离p值的算法。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-06-09 DOI: 10.1089/cmb.2024.0854

Petr Ryšavý, Filip Železný

引用次数: 0

A Mapper Algorithm with Implicit Intervals and Its Optimization. 一种隐式区间映射算法及其优化。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-06-05 DOI: 10.1089/cmb.2024.0919

Yuyang Tao, Shufei Ge

{"title":"A Mapper Algorithm with Implicit Intervals and Its Optimization.","authors":"Yuyang Tao, Shufei Ge","doi":"10.1089/cmb.2024.0919","DOIUrl":"10.1089/cmb.2024.0919","url":null,"abstract":"The Mapper algorithm is an essential tool for visualizing complex, high-dimensional data in topological data analysis and has been widely used in biomedical research. It outputs a combinatorial graph whose structure encodes the shape of the data. However, the need for manual parameter tuning and fixed (implicit) intervals, along with fixed overlapping ratios, may impede the performance of the standard Mapper algorithm. Variants of the standard Mapper algorithms have been developed to address these limitations, yet most of them still require manual tuning of parameters. Additionally, many of these variants, including the standard version found in the literature, were built within a deterministic framework and overlooked the uncertainty inherent in the data. To relax these limitations, in this work, we introduce a novel framework that implicitly represents intervals through a hidden assignment matrix, enabling automatic parameter optimization via stochastic gradient descent (SGD). In this work, we develop a soft Mapper framework based on a Gaussian mixture model for flexible and implicit interval construction. We further illustrate the robustness of the soft Mapper algorithm by introducing the Mapper graph mode as a point estimation for the output graph. Moreover, a SGD algorithm with a specific topological loss function is proposed for optimizing parameters in the model. Both simulation and application studies demonstrate its effectiveness in capturing the underlying topological structures. In addition, the application to an RNA expression dataset obtained from the Mount Sinai/JJ Peters VA Medical Center Brain Bank successfully identifies a distinct subgroup of Alzheimer's Disease. The implementation of our method is available at https://github.com/FarmerTao/Implicit-interval-Mapper.git.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"781-796"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants. Sc-TUSV-Ext：单细胞克隆谱系推断从单核苷酸变异，拷贝数改变，和结构变异。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-03-06 DOI: 10.1089/cmb.2024.0613

Nishat Anjum Bristy, Xuecong Fu, Russell Schwartz

{"title":"Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.","authors":"Nishat Anjum Bristy, Xuecong Fu, Russell Schwartz","doi":"10.1089/cmb.2024.0613","DOIUrl":"10.1089/cmb.2024.0613","url":null,"abstract":"Clonal lineage inference (\"tumor phylogenetics\") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single-cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming-based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants, copy number alterations, and structural variations into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness of real data in resolving clonal evolution in the presence of multiple variant types, providing a path toward more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"723-737"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Data Compression: Recent Innovations in LZ77 Algorithms. 增强数据压缩：LZ77算法的最新创新。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-08-01 Epub Date: 2025-05-30 DOI: 10.1089/cmb.2024.0879

Aaron Hong, Christina Boucher

{"title":"Enhancing Data Compression: Recent Innovations in LZ77 Algorithms.","authors":"Aaron Hong, Christina Boucher","doi":"10.1089/cmb.2024.0879","DOIUrl":"10.1089/cmb.2024.0879","url":null,"abstract":"The growing volume of genomic data, driven by advances in sequencing technologies, demands efficient data compression solutions. Traditional algorithms, such as Lempel-Ziv77 (LZ77), have been fundamental in offering lossless compression, yet they often fall short when applied to the highly repetitive structures typical of genomic sequences. This review explores the evolution of LZ77 and its adaptations for genomic data compression, highlighting specialized algorithms designed to handle redundancy in large-scale sequencing datasets efficiently. Innovations in this field have enhanced compression ratios and processing efficiencies leveraging intrinsic redundancy within genomic datasets. We critically examine a spectrum of LZ77-based algorithms, including newer adaptations for external and semi-external memory settings, and contrast their efficacy in managing large-scale genomic data. We conducted experiments to evaluate the performance of several algorithms, including KKP2, RLE-LZ, SE-KKP, BGone, and PFP-LZ77, on both real-world datasets from the Pizza&Chili repetitive corpus, Salmonella genomes, and human chromosome 19 genomes. These results underscore the trade-offs between time and memory consumption between algorithms. This article aims to provide a comprehensive guide on the current landscape and future directions of data compression technologies, equipping bioinformaticians and other practitioners with insight to tackle the escalating data challenges in genomics and beyond.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"761-780"},"PeriodicalIF":1.6,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network-Guided Sparse Subspace Clustering on Single-Cell Data. 单cell数据的网络引导稀疏子空间聚类。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-07-15 DOI: 10.1177/15578666251359688

Chenyang Yuan, Shunzhou Jiang, Songyun Li, Jicong Fan, Tianwei Yu

引用次数: 0