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iGly-IDN: Identifying Lysine Glycation Sites in Proteins Based on Improved DenseNet. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-28 Jianhua Jia,Genqiang Wu,Meifang Li
Lysine glycation is one of the most significant protein post-translational modifications, which changes the properties of the proteins and causes them to be dysfunctional. Accurately identifying glycation sites helps to understand the biological function and potential mechanism of glycation in disease treatments. Nonetheless, the experimental methods are ordinarily inefficient and costly, so effective
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Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-28 Hao Jiang,Jingxin Liu,You Song,Jinzhi Lei
Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cell of origin can define a measurement for quantifying different cell states, which is important in understanding the dynamics of cancer evolution, and might
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A Gene Selection Method Considering Measurement Errors. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-21 Hajoung Lee,Jaejik Kim
The analysis of gene expression data has made significant contributions to understanding disease mechanisms and developing new drugs and therapies. In such analysis, gene selection is often required for identifying informative and relevant genes and removing redundant and irrelevant ones. However, this is not an easy task as gene expression data have inherent challenges such as ultra-high dimensionality
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EnILs: A General Ensemble Computational Approach for Predicting Inducing Peptides of Multiple Interleukins. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-20 Rui Su,Jujuan Zhuang,Shuhan Liu,Di Liu,Kexin Feng
Interleukins (ILs) are a group of multifunctional cytokines, which play important roles in immune regulations and inflammatory responses. Recently, IL-6 has been found to affect the development of COVID-19, and significantly elevated levels of IL-6 cytokines have been reported in patients with severe COVID-19. IL-10 and IL-17 are anti-inflammatory and proinflammatory cytokines, respectively, which
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pyBrick-DNA: A Python-Based Environment for Automated Genetic Component Assembly. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-20 Gladys M Cavero Rozas,Jose M Cisneros Mandujano,Yomali A Ferreyra Chombo,Daniela V Moreno Rencoret,Yerko M Ortiz Mora,Martín E Gutiérrez Pescarmona,Alberto J Donayre Torres
Genetic component assembly is key in the simulation and implementation of genetic circuits. Automating this process, thus accelerating prototyping, is a necessity. We present pyBrick-DNA, a software written in Python, that assembles components for the construction of genetic circuits. pyBrick-DNA (colab.pyBrick.com) is a user-friendly environment where scientists can select genetic sequences or input
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Density and Conservation Optimization of the Generalized Masked-Minimizer Sketching Scheme. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-17 Minh Hoang,Guillaume Marçais,Carl Kingsford
Minimizers and syncmers are sketching methods that sample representative k-mer seeds from a long string. The minimizer scheme guarantees a well-spread k-mer sketch (high coverage) while seeking to minimize the sketch size (low density). The syncmer scheme yields sketches that are more robust to base substitutions (high conservation) on random sequences, but do not have the coverage guarantee of minimizers
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A Python Package itca for Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multiclass Classification. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-06 Chihao Zhang,Shihua Zhang,Jingyi Jessica Li
The itca Python package offers an information-theoretic criterion to assist practitioners in combining ambiguous outcome labels by balancing the tradeoff between prediction accuracy and classification resolution. This article provides instructions for installing the itca Python package, demonstrates how to evaluate the criterion, and showcases its application in real-world scenarios for guiding the
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A Computational Software for Training Robust Drug-Target Affinity Prediction Models: pydebiaseddta. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-01 Melİh Barsbey,Riza ÖZçelİk,Alperen Bağ,Berk Atil,Arzucan ÖZgür,Elif Ozkirimli
Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta: a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates
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A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models. J. Comput. Biol. (IF 1.7) Pub Date : 2023-11-01 Riza ÖZçelİk,Alperen Bağ,Berk Atil,Melİh Barsbey,Arzucan ÖZgür,Elif Ozkirimli
Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships. This leads the prediction performances of these models
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Shortest Hyperpaths in Directed Hypergraphs for Reaction Pathway Inference. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-31 Spencer Krieger,John Kececioglu
Signaling and metabolic pathways, which consist of chains of reactions that produce target molecules from source compounds, are cornerstones of cellular biology. Properly modeling the reaction networks that represent such pathways requires directed hypergraphs, where each molecule or compound maps to a vertex, and each reaction maps to a hyperedge directed from its set of input reactants to its set
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QR-STAR: A Polynomial-Time Statistically Consistent Method for Rooting Species Trees Under the Coalescent. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-30 Yasamin Tabatabaee,Sebastien Roch,Tandy Warnow
We address the problem of rooting an unrooted species tree given a set of unrooted gene trees, under the assumption that gene trees evolve within the model species tree under the multispecies coalescent (MSC) model. Quintet Rooting (QR) is a polynomial time algorithm that was recently proposed for this problem, which is based on the theory developed by Allman, Degnan, and Rhodes that proves the identifiability
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Gap-Sensitive Colinear Chaining Algorithms for Acyclic Pangenome Graphs. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-30 Ghanshyam Chandra,Chirag Jain
A pangenome graph can serve as a better reference for genomic studies because it allows a compact representation of multiple genomes within a species. Aligning sequences to a graph is critical for pangenome-based resequencing. The seed-chain-extend heuristic works by finding short exact matches between a sequence and a graph. In this heuristic, colinear chaining helps identify a good cluster of exact
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IncRna: The R Package for Optimizing lncRNA Identification Processes. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-25 Jan Pawel Jastrzebski,Stefano Pascarella,Aleksandra Lipka,Slawomir Dorocki
In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection
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Numerical Analysis of Split-Step Backward Euler Method with Truncated Wiener Process for a Stochastic Susceptible-Infected-Susceptible Model. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-09 Xiaochen Yang,Zhanwen Yang,Chiping Zhang
This article deals with the numerical positivity, boundedness, convergence, and dynamical behaviors for stochastic susceptible-infected-susceptible (SIS) model. To guarantee the biological significance of the split-step backward Euler method applied to the stochastic SIS model, the numerical positivity and boundedness are investigated by the truncated Wiener process. Motivated by the almost sure boundedness
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Constructing Double Cyclic Codes over F2+uF2 for DNA Codes. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-01 Arunothai Kanlaya,Chakkrid Klin-Eam
In this article, we investigate the algebraic structure of double cyclic codes of length (α,β) over F2+uF2 with u2=0 and construct DNA codes from these codes. The theory of constructing double cyclic codes suitable for DNA codes is studied. We provide the necessary and sufficient conditions for the double cyclic codes to be reversible and reversible-complement codes. As an illustration, we present
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Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set. J. Comput. Biol. (IF 1.7) Pub Date : 2023-10-01 Xianglong Liang,Hokeun Sun
Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is
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Integrating Low-Order and High-Order Correlation Information for Identifying Phage Virion Proteins. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-20 Hongliang Zou,Wanting Yu
Phage virion proteins (PVPs) play an important role in the host cell. Fast and accurate identification of PVPs is beneficial for the discovery and development of related drugs. Although wet experimental approaches are the first choice to identify PVPs, they are costly and time-consuming. Thus, researchers have turned their attention to computational models, which can speed up related studies. Therefore
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An Integration Framework of Secure Multiparty Computation and Deep Neural Network for Improving Drug-Drug Interaction Predictions. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-14 Liang Pan,Xia Xiao,Shengyun Liu,Shaoliang Peng
Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multisource data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the interinstitutional collaborations for DDI predictions. We propose multiparty computation DDI (MPCDDI), a secure MPC-based
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DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-13 Jiancheng Zhong,Pan Cui,Yihong Zhu,Qiu Xiao,Zuohang Qu
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks
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Reconstruction of Viral Variants via Monte Carlo Clustering. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-11 Akshay Juyal,Roya Hosseini,Daniel Novikov,Mark Grinshpon,Alex Zelikovsky
Identifying viral variants through clustering is essential for understanding the composition and structure of viral populations within and between hosts, which play a crucial role in disease progression and epidemic spread. This article proposes and validates novel Monte Carlo (MC) methods for clustering aligned viral sequences by minimizing either entropy or Hamming distance from consensuses. We validate
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Extracting Mutant-Affected Protein-Protein Interactions via Gaussian-Enhanced Representation and Contrastive Learning. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-07 Da Liu,Yijia Zhang,Ming Yang,Jianyuan Yuan,Wen Qu
Genetic mutations can impact protein-protein interactions (PPIs) in biomedical literature. Automated extraction of PPIs affected by gene mutations from biomedical literature can aid in evaluating the clinical importance of gene variations, which is crucial for the advancement of precision medicine. In this study, a new model called the Gaussian-enhanced representation model (GRM) is introduced for
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Protein Complex Identification Based on Heterogeneous Protein Information Network. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-06 Peixuan Zhou,Yijia Zhang,Zeqian Li,Kuo Pang,Di Zhao
Protein complexes are the foundation of all cellular activities, and accurately identifying them is crucial for studying cellular systems. The efficient discovery of protein complexes is a focus of research in the field of bioinformatics. Most existing methods for protein complex identification are based on the structure of the protein-protein interaction (PPI) network, whereas some methods attempt
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The Reasoning Engine: A Satisfiability Modulo Theories-Based Framework for Reasoning About Discrete Biological Models. J. Comput. Biol. (IF 1.7) Pub Date : 2023-09-01 Boyan Yordanov,Sara-Jane Dunn,Colin Gravill,Himanshu Arora,Hillel Kugler,Christoph M Wintersteiger
We present a framework called the Reasoning Engine, which implements Satisfiability Modulo Theories (SMT)-based methods within a unified computational environment to address diverse biological analysis problems. The Reasoning Engine was used to reproduce results from key scientific studies, as well as supporting new research in stem cell biology. The framework utilizes an intermediate language for
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Creating and Using Minimizer Sketches in Computational Genomics. J. Comput. Biol. (IF 1.7) Pub Date : 2023-08-30 Hongyu Zheng,Guillaume Marçais,Carl Kingsford
Processing large data sets has become an essential part of computational genomics. Greatly increased availability of sequence data from multiple sources has fueled breakthroughs in genomics and related fields but has led to computational challenges processing large sequencing experiments. The minimizer sketch is a popular method for sequence sketching that underlies core steps in computational genomics
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BLASTphylo: An Interactive Web Tool for Taxonomic and Phylogenetic Analysis of Prokaryotic Genes. J. Comput. Biol. (IF 1.7) Pub Date : 2023-08-25 Susanne Zabel,Jennifer Müller,Friedrich Götz,Kay Nieselt
Identifying a protein's function is crucial to reveal its role in the cellular complex. Computationally, the most common approach is to search for homologous proteins in a large database of proteins of known function using BLAST. One goal of such an analysis is the identification and visualization of the protein in the taxonomy of interest. Another goal is the reconstruction of the phylogenetic history
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HF-DDI: Predicting Drug-Drug Interaction Events Based on Multimodal Hybrid Fusion. J. Comput. Biol. (IF 1.7) Pub Date : 2023-08-18 An Huang,Xiaolan Xie,Xiaojun Yao,Huanxiang Liu,Xiaoqi Wang,Shaoliang Peng
Drug-drug interactions (DDIs) can have a significant impact on patient safety and health. Predicting potential DDIs before administering drugs to patients is a critical step in drug development and can help prevent adverse drug events. In this study, we propose a novel method called HF-DDI for predicting DDI events based on various drug features, including molecular structure, target, and enzyme information
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SWsnn: A Novel Simulator for Spiking Neural Networks. J. Comput. Biol. (IF 1.7) Pub Date : 2023-08-16 Zhichao Wang,Xuelei Li,Jianping Fan,Jintao Meng,Zhenli Lin,Yi Pan,Yanjie Wei
Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit
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Automatic International Classification of Diseases Coding via Note-Code Interaction Network with Denoising Mechanism. J. Comput. Biol. (IF 1.7) Pub Date : 2023-08-01 Xiaobo Li,Yijia Zhang,Xingwang Li,Xianwei Pan,Jian Wang,Mingyu Lu
Clinical notes are comprehensive files containing explicit information about a patient's visit. However, accurately assigning medical codes from clinical documents can be a persistent challenge due to the complexity of clinical data and the vast range of medical codes. Moreover, the large volume of medical records, the noisy medical records, and the uneven quality of coders all negatively impact the
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Multigranularity Label Prediction Model for Automatic International Classification of Diseases Coding in Clinical Text. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-31 Ying Yu,Tian Qiu,Junwen Duan,Jianxin Wang
International Classification of Diseases (ICD) serves as the foundation for generating comparable global disease statistics across regions and over time. The process of ICD coding involves assigning codes to diseases based on clinical notes, which can describe a patient's condition in a standard way. However, this process is complicated by the vast number of codes and the intricate taxonomy of ICD
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A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-24 Ying Wang,Jin-Xing Liu,Juan Wang,Junliang Shang,Ying-Lian Gao
Determining the association between drug and disease is important in drug development. However, existing approaches for drug-disease associations (DDAs) prediction are too homogeneous in terms of feature extraction. Here, a novel graph representation approach based on light gradient boosting machine (GRLGB) is proposed for prediction of DDAs. After the introduction of the protein into a heterogeneous
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A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-21 Xiaoshu Zhu,Liyuan Pang,Xiaojun Ding,Wei Lan,Shuang Meng,Xiaoqing Peng
Spatial transcriptome (ST) technology provides both the spatial location and transcriptional profile of spots, as well as tissue images. ST data can be utilized to construct gene regulatory networks, which can help identify gene modules that facilitate the understanding of biological processes such as cell communication. Correlation measurement is the core basis for constructing a gene regulatory network
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Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-21 Qian Qiao,Sha-Sha Yuan,Junliang Shang,Jin-Xing Liu
The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC)
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ARGLRR: A Sparse Low-Rank Representation Single-Cell RNA-Sequencing Data Clustering Method Combined with a New Graph Regularization. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-20 Zhen-Chang Wang,Jin-Xing Liu,Jun-Liang Shang,Ling-Yun Dai,Chun-Hou Zheng,Juan Wang
The development of single-cell transcriptome sequencing technologies has opened new ways to study biological phenomena at the cellular level. A key application of such technologies involves the employment of single-cell RNA sequencing (scRNA-seq) data to identify distinct cell types through clustering, which in turn provides evidence for revealing heterogeneity. Despite the promise of this approach
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Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-19 Yi Shen,Ying-Lian Gao,Juan Wang,Bo-Xin Guan,Jin-Xing Liu
Clinical trials indicate that the dysregulation of microRNAs (miRNAs) is closely associated with the development of diseases. Therefore, predicting miRNA-disease associations is significant for studying the pathogenesis of diseases. Since traditional wet-lab methods are resource-intensive, cost-saving computational models can be an effective complementary tool in biological experiments. In this work
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Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology. J. Comput. Biol. (IF 1.7) Pub Date : 2023-07-01 Michael Robben,Mohammad Sadegh Nasr,Avishek Das,Jai Prakash Veerla,Manfred Huber,Justyn Jaworski,Jon Weidanz,Jacob Luber
The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate
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Special Issue Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC) Workshop (2022). J. Comput. Biol. (IF 1.7) Pub Date : 2023-06-05 Hyundoo Jeong,José Lugo-Martinez,Anna Ritz,Yijie Wang,Chi Zhang
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Speeding Up the Structural Analysis of Metabolic Network Models Using the Fredman-Khachiyan Algorithm B. J. Comput. Biol. (IF 1.7) Pub Date : 2023-06-01 Nafiseh Sedaghat,Tamon Stephen,Leonid Chindelevitch
The problem of computing the Elementary Flux Modes (EFMs) and Minimal Cut Sets (MCSs) of metabolic network is a fundamental one in metabolic networks. A key insight is that they can be understood as a dual pair of monotone Boolean functions (MBFs). Using this insight, this computation reduces to the question of generating from an oracle a dual pair of MBFs. If one of the two sets (functions) is known
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Positivity-Preserving Numerical Method and Relaxed Control for Stochastic Susceptible-Infected-Vaccinated Epidemic Model with Markov Switching. J. Comput. Biol. (IF 1.7) Pub Date : 2023-05-31 Zong Wang,Qimin Zhang
The stochastic susceptible-infected-vaccinated (SIV) epidemic model includes a nonlinear term, making it difficult to obtain analytical solutions. Thus, numerical approximation schemes are an important tool for predicting the dynamics of infectious diseases and establishing optimal control strategies. However, the convergence rate of the existing numerical methods [e.g., Euler-Maruyama (EM) and truncated
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Reversal and Transposition Distance on Unbalanced Genomes Using Intergenic Information. J. Comput. Biol. (IF 1.7) Pub Date : 2023-05-24 Alexsandro Oliveira Alexandrino,Andre Rodrigues Oliveira,Géraldine Jean,Guillaume Fertin,Ulisses Dias,Zanoni Dias
The most common way to calculate the rearrangement distance between two genomes is to use the size of a minimum length sequence of rearrangements that transforms one of the two given genomes into the other, where the genomes are represented as permutations using only their gene order, based on the assumption that genomes have the same gene content. With the advance of research in genome rearrangements
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A Clonal Evolution Simulator for Planning Somatic Evolution Studies. J. Comput. Biol. (IF 1.7) Pub Date : 2023-05-15 Arjun Srivatsa,Haoyun Lei,Russell Schwartz
Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with
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Sure Joint Screening for High Dimensional Cox's Proportional Hazards Model Under the Case-Cohort Design. J. Comput. Biol. (IF 1.7) Pub Date : 2023-05-03 Yi Liu,Gang Li
This study develops a sure joint feature screening method for the case-cohort design with ultrahigh-dimensional covariates. Our method is based on a sparsity-restricted Cox proportional hazards model. An iterative reweighted hard thresholding algorithm is proposed to approximate the sparsity-restricted, pseudo-partial likelihood estimator for joint screening. We rigorously show that our method possesses
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Efficient Colored de Bruijn Graph for Indexing Reads. J. Comput. Biol. (IF 1.7) Pub Date : 2023-04-28 Nozomi Hasegawa,Kana Shimizu
The colored de Bruijn graph is a variation of the de Bruijn graph that has recently been utilized for indexing sequencing reads. Although state-of-the-art methods have achieved small index sizes, they produce many read-incoherent paths that tend to cover the same regions in the source genome sequence. To solve this problem, we propose an accurate coloring method that can reduce the generation of read-incoherent
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Quantifying Cell-Type-Specific Differences of Single-Cell Datasets Using Uniform Manifold Approximation and Projection for Dimension Reduction and Shapley Additive exPlanations. J. Comput. Biol. (IF 1.7) Pub Date : 2023-04-22 Hong Seo Lim,Peng Qiu
With rapid advances in single-cell profiling technologies, larger-scale investigations that require comparisons of multiple single-cell datasets can lead to novel findings. Specifically, quantifying cell-type-specific responses to different conditions across single-cell datasets could be useful in understanding how the difference in conditions is induced at a cellular level. In this study, we present
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EnsMOD: A Software Program for Omics Sample Outlier Detection. J. Comput. Biol. (IF 1.7) Pub Date : 2023-04-12 Nathan P Manes,Jian Song,Aleksandra Nita-Lazar
Detection of omics sample outliers is important for preventing erroneous biological conclusions, developing robust experimental protocols, and discovering rare biological states. Two recent publications describe robust algorithms for detecting transcriptomic sample outliers, but neither algorithm had been incorporated into a software tool for scientists. Here we describe Ensemble Methods for Outlier
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ClassGraph: Improving Metagenomic Read Classification with Overlap Graphs. J. Comput. Biol. (IF 1.7) Pub Date : 2023-04-06 Margherita Cavattoni,Matteo Comin
Current technologies allow the sequencing of microbial communities directly from the environment without prior culturing. One of the major problems when analyzing a microbial sample is to taxonomically annotate its reads to identify the species it contains. Most methods that are currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms
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Dynamic Epidemiological Networks: A Data Representation Framework for Modeling and Tracking of SARS-CoV-2 Variants. J. Comput. Biol. (IF 1.7) Pub Date : 2023-04-01 Fiona Senchyna,Rahul Singh
The large-scale real-time sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has allowed for rapid identification of concerning variants through phylogenetic analysis. However, the nature of phylogenetic reconstruction is typically static, in that the relationships between taxonomic units, once defined, are not subject to alterations. Furthermore, most phylogenetic methods
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GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-30 Sahar Al Seesi,Anas Al-Okaily,Tatiana V Shcheglova,Elham Sherafat,Fahad H Alqahtani,Adam T Hagymasi,Anupinder Kaur,Pramod K Srivastava,Ion I Măndoiu
High-throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines
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IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-24 Anna Pačínková,Vlad Popovici
Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number
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Numerical Analysis of Linearly Implicit Methods for Discontinuous Nonlinear Gurtin-MacCamy Model. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-20 Zhijie Chen,Tianhao Yan,Zhanwen Yang
In this study, we study a nonlinear age-structured population models with discontinues mortality and fertility rates, motivated by the fact that different maturation period may cause the significant difference in rates. We develop a novel numerical method with two-layer boundary conditions, the linearly implicit θ-methods on a special mesh. With a uniform boundedness analysis of numerical solutions
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Estimation of Ordinary Differential Equation Models for Gene Regulatory Networks Through Data Cloning. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-10 Donghui Son,Jaejik Kim
Ordinary differential equations (ODEs) are widely used for elucidating dynamic processes in various fields. One of the applications of ODEs is to describe dynamics of gene regulatory networks (GRNs), which is a critical step in understanding disease mechanisms. However, estimation of ODE models for GRNs is challenging because of inflexibility of the model and noisy data with complex error structures
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Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-06 Guo Mao,Zhengbin Pang,Ke Zuo,Jie Liu
In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell
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Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms. J. Comput. Biol. (IF 1.7) Pub Date : 2023-03-01 Tunç Başar Köse,Jiarong Li,Anna Ritz
A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs
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Evaluating Compression-Based Phylogeny Estimation in the Presence of Incomplete Lineage Sorting. J. Comput. Biol. (IF 1.7) Pub Date : 2023-02-27 Deangelo Wilson,John D Rogers
This study assesses characteristics of the normalized compression distance (NCD) technique for building phylogenetic trees from molecular data. We examined results from a mammalian biological data set as well as a collection of simulated data with varying levels of incomplete lineage sorting. The implementation of NCD we analyze is a concatenation-based, distance-based, alignment-free, and model-free
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Chromosome Three-Dimensional Structure Reconstruction: An Iterative ShRec3D Algorithm. J. Comput. Biol. (IF 1.7) Pub Date : 2023-02-27 Fang-Zhen Li,Xue-Fen Zhang,Hui-Ying Cai,Ling-Qiang Ran,Hai-Yan Zhou,Zhi-E Liu
The three-dimensional (3D) structure of chromosomes is of great significance to ensure that the genome performs various functions (e.g., gene expression) correctly and replicates and separates correctly in mitosis. Since the emergence of Hi-C in 2009, a new experimental technique in molecular biology, researchers have been paying more and more attention to the reconstruction of chromosome 3D structure
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Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN. J. Comput. Biol. (IF 1.7) Pub Date : 2023-02-17 Takeyuki Tamura,Ai Muto-Fujita,Yukako Tohsato,Tomoyuki Kosaka
Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts
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Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data J. Comput. Biol. (IF 1.7) Pub Date : 2023-02-02 Prakash Chourasia, Sarwan Ali, Simone Ciccolella, Gianluca Della Vedova, Murray Patterson
The massive amount of genomic data appearing for SARS-CoV-2 since the beginning of the COVID-19 pandemic has challenged traditional methods for studying its dynamics. As a result, new methods such as Pangolin, which can scale to the millions of samples of SARS-CoV-2 currently available, have appeared. Such a tool is tailored to take as input assembled, aligned, and curated full-length sequences, such
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Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer J. Comput. Biol. (IF 1.7) Pub Date : 2023-01-30 Bikram Sahoo, Zandra Pinnix, Seth Sims, Alex Zelikovsky
With the properties of aggressive cancer and heterogeneous tumor biology, triple-negative breast cancer (TNBC) is a type of breast cancer known for its poor clinical outcome. The lack of estrogen, progesterone, and human epidermal growth factor receptor in the tumors of TNBC leads to fewer treatment options in clinics. The incidence of TNBC is higher in African American (AA) women compared with European
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Positivity Preserving Truncated Euler–Maruyama Scheme for the Stochastic Age-Structured HIV/AIDS Model J. Comput. Biol. (IF 1.7) Pub Date : 2023-01-27 Jie Ren, Huaimin Yuan, Qimin Zhang
Since the analytical solution of the stochastic age-structured human immunodeficiency virus/acquired immune deficiency syndrome model is difficult to solve, establishing an efficient numerical approximation is an important way to predict the dynamic behavior of the model. In this article, a full-discrete scheme is proposed, where the Galerkin finite element method and the positivity preserving truncated
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MAMMLE: A Framework for Phylogeny Estimation Based on Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble J. Comput. Biol. (IF 1.7) Pub Date : 2023-01-27 Muhammad Ali Nayeem, Naser Anjum Samudro, M. Saifur Rahman, M. Sohel Rahman
Motivation: Phylogenetic trees are often inferred from a multiple sequence alignment (MSA) where the tree accuracy is heavily impacted by the nature of estimated alignment. Carefully equipping an MSA tool with multiple application-aware objectives positively impacts its capability to yield better trees.
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Intra-Exon Motif Correlations as a Proxy Measure for Mean Per-Tile Sequence Quality Data in RNA-Seq J. Comput. Biol. (IF 1.7) Pub Date : 2023-01-23 Jamie J. Alnasir, Hugh P. Shanahan
Given the wide variability in the quality of next-generation sequencing data submitted to public repositories, it is essential to identify methods that can perform quality control on these data sets when additional quality control data, such as mean tile data, are missing from public repositories. In this study, we present evidence that correlating counts of reads corresponding to pairs of motifs separated