Journal of Computational Biology最新文献

Disambiguating a Soft Metagenomic Clustering.

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.

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":"https://doi.org/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

Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug. 基于药物共同治疗靶点的超图嵌入药物再利用。

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.2023.0427

Hanieh Abbasi, Amir Lakizadeh

{"title":"Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug.","authors":"Hanieh Abbasi, Amir Lakizadeh","doi":"10.1089/cmb.2023.0427","DOIUrl":"10.1089/cmb.2023.0427","url":null,"abstract":"Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"316-329"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794721","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

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network. 基于扩散的图注意网络预测药物反应。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-03-01 Epub Date: 2024-12-06 DOI: 10.1089/cmb.2024.0807

Emre Sefer

{"title":"DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.","authors":"Emre Sefer","doi":"10.1089/cmb.2024.0807","DOIUrl":"10.1089/cmb.2024.0807","url":null,"abstract":"Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"330-350"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785889","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 New Structure Feature Introduced to Predict Protein-Protein Interaction Sites.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-26 DOI: 10.1089/cmb.2024.0804

Lingwei Lai, Jing Geng, Haochen Duan, Siyuan Chen, Lvwen Huang, Jiantao Yu

引用次数: 0

Subject-Specific Dosage Estimation for Primary Hypothyroidism Using Sparse Data.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-17 DOI: 10.1089/cmb.2024.0752

Devleena Ghosh, Chittaranjan Mandal

{"title":"Subject-Specific Dosage Estimation for Primary Hypothyroidism Using Sparse Data.","authors":"Devleena Ghosh, Chittaranjan Mandal","doi":"10.1089/cmb.2024.0752","DOIUrl":"https://doi.org/10.1089/cmb.2024.0752","url":null,"abstract":"Subject-specific dosage estimation for primary hypothyroidism using subject-specific parameters of the thyrotropic regulation system is presented in this work. The data needed for such personalized modeling are usually sparse. This is addressed by utilizing available data along with domain knowledge for estimation of model parameters but with some uncertainty. Optimization-based dosage estimation approaches may not be applicable in the presence of such uncertainty. In this work, the optimal drug dosage range based on estimated parameter ranges for primary hypothyroid condition is estimated using the mathematical model through satisfiability modulo theory (SMT)-based analysis. The salient features of this work are as follows: (1) estimation of subject-specific model parameters with uncertainty using subject-specific pre-treatment and post-treatment observations, (2) modeling periodic drug administration as part of the ordinary differential equation model of thyrotropic regulation pathway through Fourier series approximation, (3) application of SMT-based analysis for determining optimal dosage range using this model and estimated parameter ranges, and (4) an initial dosage estimation method using the regression model. Results have been obtained to support the working of the developed computational procedures.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433256","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