Journal of Computational Biology最新文献

{"title":"Separating Biological Variance from Noise by Applying Expectation-Maximization Algorithm to Modified General Linear Model.","authors":"Tien-Wen Lee","doi":"10.1177/15578666251370766","DOIUrl":"10.1177/15578666251370766","url":null,"abstract":"<p><p>The general linear model (GLM) has been widely used in research, where the error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and nonbiological noise. Using the expectation and maximization (EM) scheme can distinguish biological variance from noise, termed EMSEV (EM for separating variances). The performance of EMSEV was evaluated by varying noise levels, dimensions of the design matrix, and covariance structures of the target variables. The deviation between EMSEV outputs and the predefined distribution parameters increased with noise level. With a proper initial guess, when the noise magnitude and the variance of the target variables were similar, there were deviations of 3% and 10%-16% in the estimated mean and covariance of the target variables, respectively, along with a 1.7% deviation in noise estimation. EMSEV appears promising for distinguishing signal variance from noise in biological systems. The potential applications and implications in biological science and statistical inference are discussed.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1121-1130"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12976711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Group-Penalized Exponential Tilt Model for Identification of Differentially Methylated Genes in Epigenetic Association Studies.","authors":"Dan Huang, Hyerim Park, Hokeun Sun","doi":"10.1177/15578666251380235","DOIUrl":"10.1177/15578666251380235","url":null,"abstract":"<p><p>DNA methylation is a representative epigenetic change that occurs in our body and plays an essential role in regulating gene expression as well as in cancer progression. Identification of differentially methylated genes between two biological conditions has been popularly studied in epigenetic association studies. However, most of statistical methods aim to detect differences in mean methylation levels between two conditions. So, they are limited to identify differences in methylation variances which have been recently observed in cancer research. Moreover, they often fail to identify genes containing both differentially methylated CpG sites and neutral sites due to weak group association signals. In this article, we propose a new statistical method based on a group-penalized exponential tilt model that essentially combines an exponential tilt model and group lasso, regrading each gene as a group of multiple CpG sites. The proposed method is able to detect differentially methylated genes, capturing both mean and variance association signals. In our extensive simulation study, we demonstrated that the proposed method has superior selection performance, compared with the existing statistical methods developed for detection of differentially methylated genes. We also applied it to 450K DNA methylation data of The Cancer Genome Atlas Breast Invasive Carcinoma Collection. We were able to identify potentially cancer-related genes.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1131-1146"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131110","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}

{"title":"BCtypeFinder: A Semi-Supervised Model with Domain Adaptation for Breast Cancer Subtyping Using DNA Methylation Profiles.","authors":"Joung Min Choi, Liqing Zhang","doi":"10.1177/15578666251380233","DOIUrl":"10.1177/15578666251380233","url":null,"abstract":"<p><p>Accurate breast cancer subtype prediction is critical for precise diagnosis, treatment planning, and prognosis evaluation. Recent studies highlight the important role of epigenetic modifications in breast tumor, especially the potential of abnormal DNA methylation patterns as markers for distinct subtypes. However, developing a reliable model for subtype prediction based on DNA methylation profiles is challenging due to the scarcity of annotated dataset. This work proposes BCtypeFinder, a breast cancer subtype prediction framework that utilizes a domain adaptation network combined with semi-supervised learning to address batch effects. Our model leverages both labeled and unlabeled DNA methylation data to extract domain-invariant features while aligning subtype distributions across various datasets. BCtypeFinder outperforms current methods, showcasing superior classification performance across multiple test cases. Furthermore, we explored the effects of batch correction in BCtypeFinder, demonstrating its ability to remove batch-specific variations among patients of the same subtype, thus improving the robustness of the classifier. BCtypeFinder is publicly available at https://github.com/joungmin-choi/BCtypeFinder.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1160-1170"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225388","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}

{"title":"The Asymptotic Distribution of the <i>k</i>-Robinson-Foulds Dissimilarity Measure on Labeled Trees.","authors":"Michael Fuchs, Mike Steel","doi":"10.1089/cmb.2025.0093","DOIUrl":"10.1089/cmb.2025.0093","url":null,"abstract":"<p><p>Motivated by applications in medical bioinformatics, Khayatian et al. (2024) introduced a family of metrics on Cayley trees [the <i>k</i>-Robinson-Foulds (RF) distance, for <math><mrow><mi>k</mi><mo> </mo><mo>=</mo><mo> </mo><mn>0</mn><mo>,</mo></mrow></math> . . . <math><mrow><mo>,</mo><mi>n</mi><mo>-</mo><mn>2</mn></mrow></math>] and explored their distribution on pairs of random Cayley trees via simulations. In this article, we investigate this distribution mathematically and derive exact asymptotic descriptions of the distribution of the <i>k</i>-RF metric for the extreme values <math><mrow><mi>k</mi><mo> </mo><mo>=</mo><mo> </mo><mn>0</mn></mrow></math> and <math><mrow><mi>k</mi><mo> </mo><mo>=</mo><mo> </mo><mi>n</mi><mo>-</mo><mn>2</mn></mrow></math>, as <i>n</i> becomes large. We show that a linear transform of the 0-RF metric converges to a Poisson distribution (with mean 2), whereas a similar transform for the (<math><mrow><mi>n</mi><mo>-</mo><mn>2</mn></mrow></math>)-RF metric leads to a normal distribution (with mean <math><mrow><mstyle><mo>∼</mo></mstyle><mo> </mo><mi>n</mi><mrow><msup><mrow><mi>e</mi></mrow><mrow><mo>-</mo><mn>2</mn></mrow></msup></mrow></mrow></math>). These results (together with the case <math><mrow><mi>k</mi><mo> </mo><mo>=</mo><mo> </mo><mn>1</mn></mrow></math> which behaves quite differently and <math><mrow><mi>k</mi><mo> </mo><mo>=</mo><mo> </mo><mi>n</mi><mo>-</mo><mn>3</mn></mrow></math>) shed light on the earlier simulation results and the predictions made concerning them.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1060-1073"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540425","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}

{"title":"Using Partition Information Entropy to Computationally Rank Order Critical Subreactions in a Petri Net Model of a Biochemical Signaling Network.","authors":"Janet B Jones-Oliveira, Hans-Joseph B Oliveira, Joseph S Oliveira, David A Dixon","doi":"10.1089/cmb.2024.0849","DOIUrl":"10.1089/cmb.2024.0849","url":null,"abstract":"<p><p>Improved computational methods to analyze the mathematical structure and function of biochemical networks are needed when the biomolecular connectivity is known but when a complete set of the equilibrium and rate constants may not be available. We use Petri nets, which are equivalently bipartite digraphs, to analyze the rule-based flow of information through the network. We present several computational improvements to Petri net modeling as an aid to improve this approach, previously limited by the combinatorics of network size and complexity. The generation of Petri nets using equations for three elemental stencils (molecular reaction, synthesis complex formation, and decomposition complex formation) has been automated. A set of finite probability measures is defined in terms of a partition information entropy, where the complete listing of unique minimal cycles (UMCs) of the Petri net provides the natural partitioning. This enables the ranking of the UMC listing that covers all possible information flows in the reaction network; the information entropy measure enables the identification of which UMCs are more significant than others. In terms of the information entropy, forward cycles are less surprising and carry less information entropy, whereas backward cycles carry more information entropy and serve as regulators by providing feedback to control the network. As the systems analyzed increase in size and complexity, the automatic rank ordering of the UMCs provides a mechanism to highlight the globally most important information without the need to make local simplifying modeling choices. The information entropy metric is also used to compute source-to-sink information costs and is related to knockout analyses. The hybrid Petri net approach shows the most important species and where it is easiest to disrupt or otherwise affect the network. As exemplar, the enhanced methodology is applied to a model of the initial subnetwork in the EGFR network.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1003-1040"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248128","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}

{"title":"A Spatial-Correlated Multitask Linear Mixed-Effects Model for Imaging Genetics.","authors":"Zhibin Pu, Shufei Ge","doi":"10.1089/cmb.2024.0721","DOIUrl":"10.1089/cmb.2024.0721","url":null,"abstract":"<p><p>Imaging genetics aims to uncover the hidden relationship between imaging quantitative traits (QTs) and genetic markers [e.g., single nucleotide polymorphism (SNP)] and brings valuable insights into the pathogenesis of complex diseases, such as cancers and cognitive disorders (e.g., Alzheimer's disease). However, most linear models in imaging genetics did not explicitly model the inner relationship among QTs, which might miss some potential efficiency gains from information borrowing across brain regions. In this work, we developed a novel Bayesian regression framework for identifying significant associations between QTs and genetic markers while explicitly modeling spatial dependency between QTs, with the main contributions as follows. First, we developed a spatial-correlated multitask linear mixed-effects model to account for dependencies between QTs. We incorporated a population-level mixed-effects term into the model, taking full advantage of the dependent structure of brain imaging-derived QTs. Second, we implemented the model in the Bayesian framework and derived a Markov chain Monte Carlo (MCMC) algorithm to achieve the model inference. Further, we incorporated the MCMC samples with the Cauchy combination test to examine the association between SNPs and QTs, which avoided computationally intractable multitest issues. The simulation studies indicated improved power of our proposed model compared with classical models where inner dependencies of QTs were not modeled. We also applied the new spatial model to an imaging dataset obtained from the Alzheimer's Disease Neuroimaging Initiative database (https://adni.loni.usc.edu). The implementation of our method is available at https://github.com/ZhibinPU/spatialmultitasklmm.git.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"987-1002"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144234309","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}

{"title":"RadSpliceDB: A Comprehensive Database of Radiotherapy Prognosis-Related Splicing Isoforms.","authors":"Ling-Yu Wu, Yan Shao, Yang Gao, Xun-Jie Li, Xing-Xing Kang, Guo-Ping Zhao, Peng-Bo Wen","doi":"10.1177/15578666251363380","DOIUrl":"10.1177/15578666251363380","url":null,"abstract":"<p><p>Radiotherapy (RT) plays a crucial role in tumor treatment, but reliable prognostic biomarkers for patient survival remain limited. To address this gap, we constructed a comprehensive database, RadSpliceDB (https://radsplicedb.com.cn), focusing on splicing isoforms, aiming to identify potential prognostic markers from this perspective. We integrated transcriptome data from patients treated with RT across 24 tumor types in The Cancer Genome Atlas to identify splicing isoforms associated with RT prognosis. We constructed effective prognostic models to validate the potential of the selected isoforms as reliable biomarkers. The database provides comprehensive annotations and functional analyses of these isoforms. RadSpliceDB contains a total of 49,587 splicing events associated with RT prognosis, covering 180,149 splicing isoforms and encompassing various common splicing patterns, such as exon skipping, 3' splice site, and 5' splice site. We further evaluated the potential of these splicing isoforms as tumor antigens using VaxiJen v2.0, identifying several candidates with high antigenicity scores. This database not only provides systematic annotations of splicing isoforms to elucidate the mechanisms of RT response but also serves as a valuable resource for identifying potential biomarkers for personalized RT. RadSpliceDB provides essential data support for optimizing RT strategies in cancer treatment.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1090-1099"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855397","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}

{"title":"Microbe Drug Association Prediction with Bernoulli Random Forests.","authors":"Jia Qu, Qing-Nuo Li, Zi-Hao Song, Jin-Cheng Zhao, Qing-Gang Bu, Ze-Kang Bian, Wan-Ling Xie","doi":"10.1177/15578666251372198","DOIUrl":"10.1177/15578666251372198","url":null,"abstract":"<p><p>Due to the widespread use of antibiotics, many microbes have become drug-resistant. It is urgent to develop new antibiotics that can effectively combat drug-resistant microbes. Exploiting microbe-drug associations can help researchers make progress in drug development. In this paper, we develop for the first time a computational model of Bernoulli random forest (BRF) for microbe-drug association (BRFMDA) prediction. First, we introduced integrated drug similarity and integrated microbe similarity to construct feature of each microbe-drug pair. Second, based on known microbe-drug association, we obtained the features of all positive sample. Then, the same number of negative samples as the number of positive samples were chosen from unknown microbe-drug pairs. Next, we used a filter-based approach to reduce the dimension of features of positive and negative samples. Lastly, BRF was used to train features of positive and negative samples to predict microbe-drug associations. For validating the performance of BRFMDA, we took leave-one-out cross-validation (LOOCV) and fivefold cross-validation, as well as two types of case studies, to validate the prediction performance of BRFMDA. The results of cross-validation and case studies suggested that BRFMDA is a dependable model for predicting potential microbe-drug associations. Specifically, on the Microbe-Drug Association Database (MDAD), BRFMDA obtained an area under the curve (AUC) of 0.9134 in global LOOCV, 0.8958 in local LOOCV, and 0.8657 ± 0.0112 in fivefold cross-validation. On the abiofilm dataset, BRFMDA achieved an AUC of 0.9130 in global LOOCV, 0.8927 in local LOOCV, and 0.8844 ± 0.0137 in fivefold cross-validation.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1074-1089"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145033432","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}

{"title":"RNAS-sgRNA: Recurrent Neural Architecture Search for Detection of On-Target Effects in Single Guide RNA.","authors":"Shehla Rafiq, Assif Assad","doi":"10.1089/cmb.2025.0031","DOIUrl":"10.1089/cmb.2025.0031","url":null,"abstract":"<p><p>Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 is a leading genomic editing tool, but its effectiveness is limited by considerable heterogeneity in target efficiency among different single guide RNAs (sgRNA). This study presents RNAS-sgRNA, a hybrid model that integrates neural architecture search (NAS) with recurrent neural networks (RNN) to evaluate the on-target efficacy of CRISPR/Cas9 sgRNA. The RNAS-sgRNA model automates architectural discovery, improving sgRNA sequence categorization without considerable manual adjustment. The NAS component improves the RNN architecture, which analyzes sgRNA sequences represented as binary matrices and produces a classification score. Upon evaluation across several datasets, RNAS-sgRNA exhibits substantial performance enhancements with multiple cell lines, comparing its area under the receiver operating characteristic curve (AUROC) performance to the baseline CRISPRpred(SEQ) and DeepCRISPR models. RNAS-sgRNA demonstrated substantial improvements in AUROC performance in several cell lines compared with existing models. Notable improvements include enhancements of 8.62% for HCT116, 121.57% for HEK293T, 13.40% for HeLa, and 20.78% for HL60 cell lines, resulting in an overall improvement of 13.46%. Compared with DeepCRISPR, the model achieved additional AUROC gains in all cell lines tested, with an average improvement of 14.74%. The study also highlighted the ability of the model to deliver superior performance on smaller datasets through transfer learning, underscoring its potential applications in personalized medicine and genetic research. RNAS-sgRNA introduces a novel integration of NAS with RNN to evaluate the efficacy of CRISPR/Cas9 sgRNA. Unlike traditional methods that require significant manual adjustments, this model automates architectural discovery, optimizing the RNN structure for sgRNA sequence analysis. Furthermore, the application of transfer learning to fine-tune the pretrained model on small cell-line datasets represents a pioneering approach in the domain. The model's demonstrated ability to significantly outperform existing algorithms, including CRISPRpred(SEQ) and DeepCRISPR, across multiple cell lines highlights its innovative contribution to genomic editing research and personalized medicine.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1041-1059"},"PeriodicalIF":1.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144275029","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}

{"title":"<i>Corrigendum to:</i> CerviNet: A Novel Approach for Cervical Cancer Classification Using Pap-Smear Images.","authors":"","doi":"10.1177/15578666251387096","DOIUrl":"https://doi.org/10.1177/15578666251387096","url":null,"abstract":"","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"32 10","pages":"986"},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274597","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}