Journal of Computational Biology最新文献

Multichannel Contribution Aware Network for Prostate Cancer Grading in Histopathology Images. 组织病理图像中前列腺癌分级的多通道贡献感知网络。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-28 DOI: 10.1089/cmb.2024.0872

Junlai Qiu, Qingfeng Chen, Wei Lan, Junyue Cao

{"title":"Multichannel Contribution Aware Network for Prostate Cancer Grading in Histopathology Images.","authors":"Junlai Qiu, Qingfeng Chen, Wei Lan, Junyue Cao","doi":"10.1089/cmb.2024.0872","DOIUrl":"https://doi.org/10.1089/cmb.2024.0872","url":null,"abstract":"Gleason grading of prostate histopathology images is widely used by pathologists for diagnosis and prognosis. Spatial characteristics of cell and tissues through staining images is essential for accurate grading of prostate cancer. Although considerable efforts have been made to train grading models, they mainly rely on basic preprocessed images and largely overlook the intricate multiple staining aspects of histopathology images that are crucial for spatial information capture. This article proposes a novel deep learning model for automated prostate cancer grading by integrating several staining characteristics. Image deconvolution is applied to separate the multiple staining channels in the histopathology image, thereby enabling the model to identify effective feature information. A channel and pixel attention-based encoder is designed to extract cell and tissue structure information from multiple staining channel images. We propose a dual-branch decoder, where the classical convolutional neural network branch specializes in local feature extraction and the Transformer branch focuses on global feature extraction, to effectively fuse and refine features from different staining channels. Taking full advantage of the complementarity of multiple staining channels makes the features more compact and discriminative, leading to precise grading. Extensive experiments on relevant public datasets demonstrate the effectiveness and scalability of the proposed model.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730222","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

Recursive Clustering of Cellular Diversity in scRNA-Seq Data. scRNA-Seq数据中细胞多样性的递归聚类。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-28 DOI: 10.1089/cmb.2024.0625

Michael Squires, Peng Qiu

{"title":"Recursive Clustering of Cellular Diversity in scRNA-Seq Data.","authors":"Michael Squires, Peng Qiu","doi":"10.1089/cmb.2024.0625","DOIUrl":"https://doi.org/10.1089/cmb.2024.0625","url":null,"abstract":"In scRNA-seq analysis, cell clusters are typically defined by a single round of feature extraction and clustering. This approach may miss phenotypic differences in cell types that are characterized by genes not sufficiently represented in the feature set derived using all cells, such as rare cell types. This work explores an alternative approach, where cell clusters are identified by recursively performing feature extraction and clustering on previously identified clusters, such that each subclustering step uses features that are more specific to distinguishing the higher-resolution subclusters. We benchmark this recursive approach against the conventional, nonrecursive clustering approach and demonstrate that the recursive method results in robust improvement in cell type detection on four scRNA-seq datasets across a wide range of clustering resolution parameters. We apply the recursive approach to cluster scRNA-seq data obtained from patients with Crohn's disease belonging to three clinical phenotypes and observe that recursive clustering captures phenotypic differences only visible at specific levels of granularity within an interpretable hierarchical framework while defining cell clusters within a gene expression feature space more specific to each cluster.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143730225","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

On the Relation Between Linear Autoencoders and Non-Negative Matrix Factorization for Mutational Signature Extraction. 线性自编码器与非负矩阵分解在突变签名提取中的关系。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-21 DOI: 10.1089/cmb.2024.0784

Ida Egendal, Rasmus Froberg Brøndum, Marta Pelizzola, Asger Hobolth, Martin Bøgsted

{"title":"On the Relation Between Linear Autoencoders and Non-Negative Matrix Factorization for Mutational Signature Extraction.","authors":"Ida Egendal, Rasmus Froberg Brøndum, Marta Pelizzola, Asger Hobolth, Martin Bøgsted","doi":"10.1089/cmb.2024.0784","DOIUrl":"10.1089/cmb.2024.0784","url":null,"abstract":"Since its introduction, non-negative matrix factorization (NMF) has been a popular tool for extracting interpretable, low-dimensional representations of high-dimensional data. However, several recent studies have proposed replacing NMF with autoencoders. The increasing popularity of autoencoders warrants an investigation on whether this replacement is in general valid and reasonable. Moreover, the exact relationship between non-negative autoencoders and NMF has not been thoroughly explored. Thus, a main aim of this study is to investigate in detail the relationship between autoencoders and NMF. We define a non-negative linear autoencoder, AE-NMF, which is mathematically equivalent with convex NMF, a constrained version of NMF. The performance of NMF and the non-negative linear autoencoder is compared within the context of mutational signature extraction from simulated and real-world cancer genomics data. We find that the reconstructions based on NMF are more accurate compared with AE-NMF, while the signatures extracted using both methods exhibit comparable consistency and performance when externally validated. These findings suggest that AE-NMF, the linear non-negative autoencoders investigated in this article, do not provide an improvement of NMF in the field of mutational signature extraction. Our study serves as a foundation for understanding the theoretical implication of replacing NMF with non-negative autoencoders.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143669971","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

Compressed Representation of Extreme Learning Machine with Self-Diffusion Graph Denoising Applied for Dissecting Molecular Heterogeneity. 基于自扩散图去噪的极限学习机压缩表示在分子异质性解剖中的应用。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-19 DOI: 10.1089/cmb.2024.0729

Xin Duan, Xinnan Ding, Yuelin Lu

{"title":"Compressed Representation of Extreme Learning Machine with Self-Diffusion Graph Denoising Applied for Dissecting Molecular Heterogeneity.","authors":"Xin Duan, Xinnan Ding, Yuelin Lu","doi":"10.1089/cmb.2024.0729","DOIUrl":"https://doi.org/10.1089/cmb.2024.0729","url":null,"abstract":"Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profiles has been widely used to dissect molecular heterogeneity. One drawback common to most clustering methods is that they often suffer from high dimensionality and noise, as well as feature redundancy. To address these challenges, we propose Extreme learning machine self-diffusion (ELMSD), an auto-encoder extreme learning machine feature representation method that incorporates a self-diffusion graph denoising framework to effectively dissect molecular heterogeneity. Our method, ELMSD, first learns a compressed representation of gene expression profiles from the hidden layer of the autoencoder extreme learning machine, followed by an iterative graph diffusion process to enhance the sample-to-sample similarity. The enhanced graph can largely facilitate the downstream clustering analysis, making it more efficient to analyze molecular properties. To demonstrate the utility of ELMSD, we applied it on one simulation dataset, five single-cell datasets, and 20 cancer datasets. Experiment results show that the ELMSD approach outperforms several state-of-the-art clustering methods and cancer subtypes, cell types identified by ELMSD reveal strong clinical relevance and biological interpretation. The ELMSD code is available at: https://github.com/DXCODEE/ELMSD.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657382","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

Disambiguating a Soft Metagenomic Clustering. 软宏基因组聚类的消歧。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-07 DOI: 10.1089/cmb.2024.0825

Rahul Nihalani, Jaroslaw Zola, Srinivas Aluru

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 1.4 4区生物学

Journal of Computational Biology Pub 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":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-06","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

An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging. 在活细胞成像中，基于距离的土工自监督框架用于细胞动态分级。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-01 Epub Date: 2024-12-02 DOI: 10.1089/cmb.2024.0672

Fengqian Pang, Chunyue Lei, Hongfei Zhao, Zhiqiang Xing

{"title":"An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging.","authors":"Fengqian Pang, Chunyue Lei, Hongfei Zhao, Zhiqiang Xing","doi":"10.1089/cmb.2024.0672","DOIUrl":"10.1089/cmb.2024.0672","url":null,"abstract":"Cellular appearance and its dynamics frequently serve as a proxy measurement of live-cell physiological properties. The computational analysis of cell properties is considered to be a significant endeavor in biological and biomedical research. Deep learning has garnered considerable success across various fields. In light of this, various neural networks have been developed to analyze live-cell microscopic videos and capture cellular dynamics with biological significance. Specifically, cellular dynamic grading (CDG) is the task that provides a predefined dynamic grade for a live-cell according to the speed of cellular deformation and intracellular movement. This task involves recording the morphological and cytoplasmic dynamics in live-cell microscopic videos. Similar to other medical image processing tasks, CDG faces challenges in collecting and annotating cellular videos. These deficiencies in medical data limit the performance of deep learning models. In this article, we propose a novel self-supervised framework to overcome these limitations for the CDG task. Our framework relies on the assumption that increasing or decreasing cell dynamic grades is consistent with accelerating or decelerating cell appearance change in videos, respectively. This consistency is subsequently incorporated as a constraint in the loss function for the self-supervised training strategy. Our framework is implemented by formulating a probability transition matrix based on the Earth Mover's Distance and imposing a loss constraint on the elements of this matrix. Experimental results demonstrate that our proposed framework enhances the model's ability to learn spatiotemporal dynamics. Furthermore, our framework outperforms the existing methods on our cell video database.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"274-297"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769460","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

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes. 基于图的机器学习方法结合光学测量来理解心肌细胞的跳动动力学。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-01 Epub Date: 2024-12-09 DOI: 10.1089/cmb.2024.0491

Ziqian Wu, Jiyoon Park, Paul R Steiner, Bo Zhu, John X J Zhang

引用次数: 0

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics. 基于模糊识别的过渡细胞推断单细胞转录组学的细胞轨迹。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-01 Epub Date: 2024-12-13 DOI: 10.1089/cmb.2023.0432

Xiang Chen, Yibing Ma, Yongle Shi, Bai Zhang, Hanwen Wu, Jie Gao

{"title":"Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.","authors":"Xiang Chen, Yibing Ma, Yongle Shi, Bai Zhang, Hanwen Wu, Jie Gao","doi":"10.1089/cmb.2023.0432","DOIUrl":"10.1089/cmb.2023.0432","url":null,"abstract":"With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable insights into understanding cell cycle and differentiation. During cell development, cells exhibit both stable and transition states, which makes it challenging to accurately identify these cells. To address this challenge, we propose a novel single-cell trajectory inference method using fuzzy clustering, named scFCTI. By introducing fuzzy clustering and quantifying cell uncertainty, scFCTI can identify transition cells within unstable cell states. Moreover, scFCTI can obtain refined cell classification by characterizing different cell stages, which gain more accurate single-cell trajectory reconstruction containing transition paths. To validate the effectiveness of scFCTI, we conduct experiments on five real datasets and four different structure simulation datasets, comparing them with several state-of-the-art trajectory inference methods. The results demonstrate that scFCTI outperforms these methods by successfully identifying unstable cell clusters and obtaining more accurate cell paths with transition states. Especially the experimental results demonstrate that scFCTI can reconstruct the cell trajectory more precisely.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"253-273"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818363","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

Adaptive Arithmetic Coding-Based Encoding Method Toward High-Density DNA Storage. 基于自适应算术编码的编码方法，迈向高密度 DNA 存储。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-01 Epub Date: 2024-11-15 DOI: 10.1089/cmb.2024.0697

Yingxin Hu, Yanjun Liu, Yuefei Yang

{"title":"Adaptive Arithmetic Coding-Based Encoding Method Toward High-Density DNA Storage.","authors":"Yingxin Hu, Yanjun Liu, Yuefei Yang","doi":"10.1089/cmb.2024.0697","DOIUrl":"10.1089/cmb.2024.0697","url":null,"abstract":"With the rapid advancement of big data and artificial intelligence technologies, the limitations inherent in traditional storage media for accommodating vast amounts of data have become increasingly evident. DNA storage is an innovative approach harnessing DNA and other biomolecules as storage mediums, endowed with superior characteristics including expansive capacity, remarkable density, minimal energy requirements, and unparalleled longevity. Central to the efficient DNA storage is the process of DNA coding, whereby digital information is converted into sequences of DNA bases. A novel encoding method based on adaptive arithmetic coding (AAC) has been introduced, delineating the encoding process into three distinct phases: compression, error correction, and mapping. Prediction by Partial Matching (PPM)-based AAC in the compression phase serves to compress data and enhance storage density. Subsequently, the error correction phase relies on octal Hamming code to rectify errors and safeguard data integrity. The mapping phase employs a \"3-2 code\" mapping relationship to ensure adherence to biochemical constraints. The proposed method was verified by encoding different formats of files such as text, pictures, and audio. The results indicated that the average coding density of bases can be up to 3.25 per nucleotide, the GC content (which includes guanine [G] and cytosine [C]) can be stabilized at 50% and the homopolymer length is restricted to no more than 2. Simulation experimental results corroborate the method's efficacy in preserving data integrity during both reading and writing operations, augmenting storage density, and exhibiting robust error correction capabilities.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"298-315"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621431","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