Journal of Computational Biology最新文献_第8页

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation. 用于生成放射报告的视觉重新校准和选通增强网络。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-06-05 DOI: 10.1089/cmb.2024.0514

Xiaodi Hou, Guoming Sang, Zhi Liu, Xiaobo Li, Yijia Zhang

引用次数: 0

Boolean Network Models of Human Preimplantation Development. 人类胚胎植入前发育的布尔网络模型

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-29 DOI: 10.1089/cmb.2024.0517

Mathieu Bolteau, Lokmane Chebouba, Laurent David, Jérémie Bourdon, Carito Guziolowski

{"title":"Boolean Network Models of Human Preimplantation Development.","authors":"Mathieu Bolteau, Lokmane Chebouba, Laurent David, Jérémie Bourdon, Carito Guziolowski","doi":"10.1089/cmb.2024.0517","DOIUrl":"10.1089/cmb.2024.0517","url":null,"abstract":"\u0000 Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"513-523"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141179790","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

Special Issue, Part I 19th International Symposium on Bioinformatics Research and Applications (ISBRA 2023). 特刊，第 I 部分第 19 届生物信息学研究与应用国际研讨会（ISBRA 2023）。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-06-13 DOI: 10.1089/cmb.2024.0636

Murray Patterson

引用次数: 0

A Branch-and-Bound Algorithm for the Molecular Ordered Covering Problem. 分子有序覆盖问题的分支与边界算法

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-22 DOI: 10.1089/cmb.2024.0522

Michael Souza, Nilton Maia, Rômulo S Marques, Carlile Lavor

{"title":"A Branch-and-Bound Algorithm for the Molecular Ordered Covering Problem.","authors":"Michael Souza, Nilton Maia, Rômulo S Marques, Carlile Lavor","doi":"10.1089/cmb.2024.0522","DOIUrl":"10.1089/cmb.2024.0522","url":null,"abstract":"The Discretizable Molecular Distance Geometry Problem (DMDGP) plays a key role in the construction of three-dimensional molecular structures from interatomic distances acquired through nuclear magnetic resonance (NMR) spectroscopy, with the primary objective of validating a sequence of distance constraints related to NMR data. This article addresses the escalating complexity of the DMDGP encountered with larger and more flexible molecules by introducing a novel strategy via the Molecular Ordered Covering Problem, which optimizes the ordering of distance constraints to improve computational efficiency in DMDGP resolution. This approach utilizes a specialized Branch-and-Bound (BB) algorithm, tested on both synthetic and actual protein structures from the protein data bank. Our analysis demonstrates the efficacy of the previously proposed greedy heuristic in managing complex molecular scenarios, highlighting the BB algorithm's utility as a validation mechanism. This research contributes to ongoing efforts in molecular structure analysis, with possible implications for areas such as protein folding, drug design, and molecular modeling.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"475-485"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076140","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

Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction. 系统发育和化学探测信息作为 RNA 二级结构预测的软约束。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 DOI: 10.1089/cmb.2024.0519

Sarah von Löhneysen, Thomas Spicher, Yuliia Varenyk, Hua-Ting Yao, Ronny Lorenz, Ivo Hofacker, Peter F Stadler

{"title":"Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction.","authors":"Sarah von Löhneysen, Thomas Spicher, Yuliia Varenyk, Hua-Ting Yao, Ronny Lorenz, Ivo Hofacker, Peter F Stadler","doi":"10.1089/cmb.2024.0519","DOIUrl":"10.1089/cmb.2024.0519","url":null,"abstract":"Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"31 6","pages":"549-563"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141468464","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 Comparative Study of Gene Co-Expression Thresholding Algorithms. 基因共表达阈值算法比较研究

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-23 DOI: 10.1089/cmb.2024.0509

Carissa Bleker, Stephen K Grady, Michael A Langston

引用次数: 0

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering. 基于深度学习的单细胞 RNA 测序数据聚类融合学习模型。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-06-01 Epub Date: 2024-05-20 DOI: 10.1089/cmb.2024.0512

Tian-Jing Qiao, Feng Li, Sha-Sha Yuan, Ling-Yun Dai, Juan Wang

{"title":"A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.","authors":"Tian-Jing Qiao, Feng Li, Sha-Sha Yuan, Ling-Yun Dai, Juan Wang","doi":"10.1089/cmb.2024.0512","DOIUrl":"10.1089/cmb.2024.0512","url":null,"abstract":"\u0000 Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"576-588"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957730","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 Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation. 细胞分化过程中动态替代转录起始位点使用的贝叶斯变化点模型

IF 1.7 4区生物学

Journal of Computational Biology Pub Date : 2024-05-01 Epub Date: 2024-05-14 DOI: 10.1089/cmb.2023.0174

Juan Xia, Yuxia Li, Haotian Zhu, Feiyang Xue, Feng Shi, Nana Li

{"title":"A Bayesian Change Point Model for Dynamic Alternative Transcription Start Site Usage During Cellular Differentiation.","authors":"Juan Xia, Yuxia Li, Haotian Zhu, Feiyang Xue, Feng Shi, Nana Li","doi":"10.1089/cmb.2023.0174","DOIUrl":"10.1089/cmb.2023.0174","url":null,"abstract":"ABSTRACT An alternative transcription start site (ATSS) is a major driving force for increasing the complexity of transcripts in human tissues. As a transcriptional regulatory mechanism, ATSS has biological significance. Many studies have confirmed that ATSS plays an important role in diseases and cell development and differentiation. However, exploration of its dynamic mechanisms remains insufficient. Identifying ATSS change points during cell differentiation is critical for elucidating potential dynamic mechanisms. For relative ATSS usage as percentage data, the existing methods lack sensitivity to detect the change point for ATSS longitudinal data. In addition, some methods have strict requirements for data distribution and cannot be applied to deal with this problem. In this study, the Bayesian change point detection model was first constructed using reparameterization techniques for two parameters of a beta distribution for the percentage data type, and the posterior distributions of parameters and change points were obtained using Markov Chain Monte Carlo (MCMC) sampling. With comprehensive simulation studies, the performance of the Bayesian change point detection model is found to be consistently powerful and robust across most scenarios with different sample sizes and beta distributions. Second, differential ATSS events in the real data, whose change points were identified using our method, were clustered according to their change points. Last, for each change point, pathway and transcription factor motif analyses were performed on its differential ATSS events. The results of our analyses demonstrated the effectiveness of the Bayesian change point detection model and provided biological insights into cell differentiation.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"445-457"},"PeriodicalIF":1.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140943652","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

Enforcing Temporal Consistency in Migration History Inference. 在迁移历史推断中强化时间一致性。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-05-01 Epub Date: 2024-05-16 DOI: 10.1089/cmb.2023.0352

Mrinmoy Saha Roddur, Sagi Snir, Mohammed El-Kebir

{"title":"Enforcing Temporal Consistency in Migration History Inference.","authors":"Mrinmoy Saha Roddur, Sagi Snir, Mohammed El-Kebir","doi":"10.1089/cmb.2023.0352","DOIUrl":"10.1089/cmb.2023.0352","url":null,"abstract":"\u0000 In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.\u0000 ","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"396-415"},"PeriodicalIF":1.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957697","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 Integer Linear Programming Model to Optimize Coding DNA Sequences By Joint Control of Transcript Indicators. 通过联合控制转录指标优化 DNA 编码序列的整数线性规划模型。

IF 1.7 4区生物学

Journal of Computational Biology Pub Date : 2024-05-01 Epub Date: 2024-04-30 DOI: 10.1089/cmb.2023.0166

Claudio Arbib, Andrea D'ascenzo, Fabrizio Rossi, Daniele Santoni

引用次数: 0