Journal of Computational Biology最新文献_第4页

Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis. 基于邻域二元量子态粒子群优化和通量平衡分析的代谢物生产优化。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1089/cmb.2024.0538

Lidan Bai, Jun Sun, Vasile Palade, Chao Li, Hengyang Lu, Cong Gao

{"title":"Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis.","authors":"Lidan Bai, Jun Sun, Vasile Palade, Chao Li, Hengyang Lu, Cong Gao","doi":"10.1089/cmb.2024.0538","DOIUrl":"10.1089/cmb.2024.0538","url":null,"abstract":"Metabolic engineering is a rapidly evolving field that involves optimizing microbial cell factories to overproduce various industrial products. To achieve this, several tools, leveraging constraint-based stoichiometric models and metaheuristic algorithms like particle swarm optimization (PSO), have been developed. However, PSO can potentially get trapped in local optima. Quantum-behaved PSO (QPSO) overcomes this limitation, and our study further enhances its binary version (BQPSO) with a neighborhood topology, leading to the advanced neighborhood-based BQPSO (NBQPSO). Combined with flux balance analysis (FBA), this forms an innovative approach, NBQPSO-FBA, for identifying optimal knockout strategies to maximize the desired metabolite production. Additionally, we introduced a novel encoding strategy suitable for large-scale genome-scale metabolic models (GSMMs). Evaluated on four E. coli GSMMs (iJR904, iAF1260, iJO1366, and iML1515), NBQPSO-FBA matches or surpasses established bi-level linear programming (LP) and heuristic methods in metabolite production optimization. Notably, it achieved 90.69% realization of the theoretical maximum in acetate production and demonstrated comparable performance with leading algorithms in lactate production. The efficiency of NBQPSO-FBA, which requires fewer knockouts, makes it a practical and effective tool for optimizing microbial cell factories. This addresses the rising demand for microbial products across various industries.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"64-88"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142800309","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

Acknowledgment of Reviewers 2024. 审稿人致谢

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 DOI: 10.1089/cmb.2024.10852.revack

引用次数: 0

An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology. 一种结合生殖系和体细胞突变知识的分析方法增强了精确肿瘤学中的肿瘤基因组再分析。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-11 DOI: 10.1089/cmb.2023.0461

Elias DeVoe, Honey V Reddi, Bradley W Taylor, Samantha Stachowiak, Jennifer L Geurts, Ben George, Reza Shaker, Raul Urrutia, Michael T Zimmermann

{"title":"An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology.","authors":"Elias DeVoe, Honey V Reddi, Bradley W Taylor, Samantha Stachowiak, Jennifer L Geurts, Ben George, Reza Shaker, Raul Urrutia, Michael T Zimmermann","doi":"10.1089/cmb.2023.0461","DOIUrl":"10.1089/cmb.2023.0461","url":null,"abstract":"Background: Expanded analysis of tumor genomics data enables current and future patients to gain more benefits, such as improving diagnosis, prognosis, and therapeutics. Methods: Here, we report tumor genomic data from 1146 cases accompanied by simultaneous expert analysis from patients visiting our oncological clinic. We developed an analytical approach that leverages combined germline and cancer genetics knowledge to evaluate opportunities, challenges, and yield of potentially medically relevant data. Results: We identified 499 cases (44%) with variants of interest, defined as either potentially actionable or pathogenic in a germline setting, and that were reported in the original analysis as variants of uncertain significance (VUS). Of the 7405 total unique tumor variants reported, 462 (6.2%) were reported as VUS at the time of diagnosis, yet information from germline analyses identified them as (likely) pathogenic. Notably, we find that a sizable number of these variants (36%-79%) had been reported in heritable disorders and deposited in public databases before the year of tumor testing. Conclusions: This finding indicates the need to develop data systems to bridge current gaps in variant annotation and interpretation and to develop more complete digital representations of actionable pathways. We outline our process for achieving such methodologic integration. Sharing genomics data across medical specialties can enable more robust, equitable, and thorough use of patient's genomics data. This comprehensive analytical approach and the new knowledge derived from its results highlight its multi-specialty value in precision oncology settings.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"89-103"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807362","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}

引用次数: 0

De Novo Antibody Design with SE(3) Diffusion. 基于SE(3)扩散的从头抗体设计。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-27 DOI: 10.1089/cmb.2024.0768

Daniel Cutting, Frédéric A Dreyer, David Errington, Constantin Schneider, Charlotte M Deane

引用次数: 0

Generative Adversarial Networks for Neuroimage Translation. 神经图像翻译的生成对抗网络。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-27 DOI: 10.1089/cmb.2024.0635

Cassandra Czobit, Reza Samavi

{"title":"Generative Adversarial Networks for Neuroimage Translation.","authors":"Cassandra Czobit, Reza Samavi","doi":"10.1089/cmb.2024.0635","DOIUrl":"https://doi.org/10.1089/cmb.2024.0635","url":null,"abstract":"Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features. In the case of generating additional neuroimages, it is advantageous to obtain unidentifiable medical data and augment smaller annotated datasets. This study proposes the development of a cycle-consistent generative adversarial network (CycleGAN) model for translating neuroimages from one field strength to another (e.g., 3 Tesla [T] to 1.5 T). This model was compared with a model based on a deep convolutional GAN model architecture. CycleGAN was able to generate the synthetic and reconstructed images with reasonable accuracy. The mapping function from the source (3 T) to the target domain (1.5 T) performed optimally with an average peak signal-to-noise ratio value of 25.69 ± 2.49 dB and a mean absolute error value of 2106.27 ± 1218.37. The codes for this study have been made publicly available in the following GitHub repository.a.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894857","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

High-Accuracy Positivity-Preserving Finite Difference Approximations of the Chemotaxis Model for Tumor Invasion. 肿瘤侵袭趋化模型的高精度正性保留有限差分近似值

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-07 DOI: 10.1089/cmb.2023.0316

Lin Zhang, Jigen Peng, Yongbin Ge, Haiyang Li, Yuchao Tang

{"title":"High-Accuracy Positivity-Preserving Finite Difference Approximations of the Chemotaxis Model for Tumor Invasion.","authors":"Lin Zhang, Jigen Peng, Yongbin Ge, Haiyang Li, Yuchao Tang","doi":"10.1089/cmb.2023.0316","DOIUrl":"10.1089/cmb.2023.0316","url":null,"abstract":"Numerical simulation of the complex evolution process for tumor invasion plays an extremely important role in-depth exploring the bio-taxis phenomena of tumor growth and metastasis. In view of the fact that low-accuracy numerical methods often have large errors and low resolution, very refined grids have to be used if we want to get high-resolution simulating results, which leads to a great deal of computational cost. In this paper, we are committed to developing a class of high-accuracy positivity-preserving finite difference methods to solve the chemotaxis model for tumor invasion. First, two unconditionally stable implicit compact difference schemes for solving the model are proposed; second, the local truncation errors of the new schemes are analyzed, which show that they have second-order accuracy in time and fourth-order accuracy in space; third, based on the proposed schemes, the high-accuracy numerical integration idea of binary functions is employed to structure a linear compact weighting formula that guarantees fourth-order accuracy and nonnegative, and then a positivity-preserving and time-marching algorithm is established; and finally, the accuracy, stability, and positivity-preserving of the proposed methods are verified by several numerical experiments, and the evolution phenomena of tumor invasion over time are numerically simulated and analyzed.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1224-1258"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380968","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

MMG4: Recognition of G4-Forming Sequences Based on Markov Model. MMG4：基于马尔可夫模型的 G4 形成序列识别。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-17 DOI: 10.1089/cmb.2024.0523

Boyuan Yu, Hao Zhang, Cong Pian, Yuanyuan Chen

{"title":"MMG4: Recognition of G4-Forming Sequences Based on Markov Model.","authors":"Boyuan Yu, Hao Zhang, Cong Pian, Yuanyuan Chen","doi":"10.1089/cmb.2024.0523","DOIUrl":"10.1089/cmb.2024.0523","url":null,"abstract":"G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1211-1223"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466641","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

The Statistics of Parametrized Syncmers in a Simple Mutation Process Without Spurious Matches. 无假匹配的简单突变过程中参数化同步器的统计。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-11-12 DOI: 10.1089/cmb.2024.0508

John L Spouge, Pijush Das, Ye Chen, Martin Frith

{"title":"The Statistics of Parametrized Syncmers in a Simple Mutation Process Without Spurious Matches.","authors":"John L Spouge, Pijush Das, Ye Chen, Martin Frith","doi":"10.1089/cmb.2024.0508","DOIUrl":"10.1089/cmb.2024.0508","url":null,"abstract":"Introduction: Often, bioinformatics uses summary sketches to analyze next-generation sequencing data, but most sketches are not well understood statistically. Under a simple mutation model, Blanca et al. analyzed complete sketches, that is, the complete set of unassembled k-mers, from two closely related sequences. The analysis extracted a point mutation parameter θ quantifying the evolutionary distance between the two sequences. Methods: We extend the results of Blanca et al. for complete sketches to parametrized syncmer sketches with downsampling. A syncmer sketch can sample k-mers much more sparsely than a complete sketch. Consider the following simple mutation model disallowing insertions or deletions. Consider a reference sequence A (e.g., a subsequence from a reference genome), and mutate each nucleotide in it independently with probability θ to produce a mutated sequence B (corresponding to, e.g., a set of reads or draft assembly of a related genome). Then, syncmer counts alone yield an approximate Gaussian distribution for estimating θ. The assumption disallowing insertions and deletions motivates a check on the lengths of A and B. The syncmer count from B yields an approximate Gaussian distribution for its length, and a p-value can test the length of B against the length of A using syncmer counts alone. Results: The Gaussian distributions permit syncmer counts alone to estimate θ and mutated sequence length with a known sampling error. Under some circumstances, the results provide the sampling error for the Mash containment index when applied to syncmer counts. Conclusions: The approximate Gaussian distributions provide hypothesis tests and confidence intervals for phylogenetic distance and sequence length. Our methods are likely to generalize to sketches other than syncmers and may be useful in assembling reads and related applications.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1195-1210"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621433","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}

引用次数: 0

Stochastic Analysis for the Dual Virus Parallel Transmission Model with Immunity Delay. 带免疫延迟的双病毒平行传播模型的随机分析

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-18 DOI: 10.1089/cmb.2024.0662

Jing Yang, Shaojuan Ma, Juan Ma, Jinhua Ran, Xinyu Bai

引用次数: 0

An R Package for Nonparametric Inference on Dynamic Populations with Infinitely Many Types. 无限多类型动态种群的非参数推断 R 软件包

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-22 DOI: 10.1089/cmb.2024.0600

Filippo Ascolani, Stefano Damato, Matteo Ruggiero

{"title":"An R Package for Nonparametric Inference on Dynamic Populations with Infinitely Many Types.","authors":"Filippo Ascolani, Stefano Damato, Matteo Ruggiero","doi":"10.1089/cmb.2024.0600","DOIUrl":"10.1089/cmb.2024.0600","url":null,"abstract":"Fleming-Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points. Previous works have fully described the relevant estimators for this problem, but current software is available only for the Wright-Fisher finite-dimensional case. Here, we provide software for the general case, overcoming some nontrivial computational challenges posed by this setting. The R package FVDDPpkg efficiently approximates the filtering and smoothing distribution for Fleming-Viot diffusions, given finite samples of individuals collected at different times. A suitable Monte Carlo approximation is also introduced in order to reduce the computational cost.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1305-1311"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466624","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