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TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-12 Minglie Li, Shusen Zhou, Tong Liu, Chanjuan Liu, Mujun Zang, Qingjun Wang
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PPRTGI: A Personalized PageRank Graph Neural Network for TF-target Gene Interaction Detection IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-07 Ke Ma, Jiawei Li, Mengyuan Zhao, Ibrahim Zamit, Bin Lin, Fei Guo, Jijun Tang
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ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-07 Weiling Li, Raunaq Malhotra, Steven Wu, Manjari Jha, Allen Rodrigo, Mary Poss, Raj Acharya
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SGLMDA: A Subgraph Learning-based Method for miRNA-disease Association Prediction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-06 Cunmei Ji, Ning Yu, Yutian Wang, Jiancheng Ni, Chunhou Zheng
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Accurate Annotation for Differentiating and Imbalanced Cell Types in Single-cell Chromatin Accessibility Data IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-05 Yuhang Jia, Siyu Li, Rui Jiang, Shengquan Chen
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Computational prediction of potential vaccine candidates from tRNA encoded peptides(tREP) using a bioinformatic workflow and molecular dynamics validations IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-01 Pallavi M. Shanthappa, Neeraj Verma, Anu George, Pawan K. Dhar, Prashanth Athri
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Evasive Spike Variants Elucidate the Preservation of T Cell Immune Response to the SARS-CoV-2 Omicron Variant IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-03-01 Arnav Solanki, James Cornette, Julia Udell, George Vasmatzis, Marc Riedel
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Parallel algorithm for discovering and comparing three-dimensional proteins patterns IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-26 Alejandro Valdes-Jim ´ enez, Miguel Reyes-Parada, Gabriel N ´ u´nez-Vivanco, Daniel Jim ˜ enez-Gonz ´ alez
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Interpretable Prediction of SARS-CoV-2 Epitope-specific TCR Recognition Using a Pre-Trained Protein Language Model IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-21 Sunyong Yoo, Myeonghyeon Jeong, Subhin Seomun, Kiseong Kim, Youngmahn Han
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MAHyNet: Parallel Hybrid Network for RNA-Protein Binding Sites Prediction Based on Multi-Head Attention and Expectation Pooling IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-16 Wei Wang, Zhenxi Sun, Dong Liu, Hongjun Zhang, Juntao Li, Xianfang Wang, Yun Zhou
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GenCoder: A Novel Convolutional Neural Network based Autoencoder for Genomic Sequence Data Compression IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-15 Sheena K. S., Madhu S. Nair
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PMDAGS: Predicting Mirna-disease Associations with Graph Nonlinear Diffusion Convolution Network and Similarities IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-15 Cheng Yan, Guihua Duan
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DinoKnot: Duplex Interaction of Nucleic acids with pseudoKnots IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-12 Tara Newman, Hiu Fung Kevin Chang, Hosna Jabbari
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SLPA-Net: a real-time recognition network for intelligent stomata localization and phenotypic analysis IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-09 Xiao-Hui Yang, Ye-Tong Wang, Ming-Hui Wu, Fan Li, Cheng-Long Zhou, Li-Jun Yang, Chen Zheng, Yong Li, Zhi Li, Si-Yi Guo, Chun-Peng Song
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A clustering method for single-cell RNA-seq data based on automatic weighting penalty and low-rank representation IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-06 Juan Wang, Zhengchang Wang, Shasha Yuan, Chunhou Zheng, Jinxing Liu, Junliang Shang
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Comparison of orchard networks using their extended μ-representation IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-02-01 Gabriel Cardona, Joan Carles Pons, Gerard Ribas, Tomas Martınez Coronado
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scCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-29 Shujie Dong, Yuansheng Liu, Yongshun Gong, Xiangjun Dong, Xiangxiang Zeng
Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously
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LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-17 Wenjing Wang, Pengyong Han, Zhengwei Li, Ru Nie, Kangwei Wang, Lei Wang, Hongmei Liao
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SMCC: A Novel Clustering Method for Single- and Multi-Omics Data Based on Co-Regularized Network Fusion IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-12 Sha Tian, Ying Yang, Yushan Qiu, Quan Zou
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Learning from an Artificial Neural Network in Phylogenetics IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-10 Alina F. Leuchtenberger, Arndt von Haeseler
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Flanked Block-Interchange Distance on Strings IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-09 Tiantian Li, Haitao Jiang, Binhai Zhu, Lusheng Wang, Daming Zhu
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A Survey of Deep Learning for Detecting miRNA-Disease Associations: Databases, Computational Methods, Challenges, and Future Directions IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-09 Nan Sheng, Xuping Xie, Yan Wang, Lan Huang, Shuangquan Zhang, Ling Gao
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SADR: Self-supervised Graph Learning with Adaptive Denoising for Drug Repositioning IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-08 Sichen Jin, Yijia Zhang, Huimin Yu, Mingyu Lu
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Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-08 Zeqian Li, Yijia Zhang, Peixuan Zhou
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GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-05 Daniel Manu, Jingjing Yao, Wuji Liu, Xiang Sun
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NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-04 Zhao-Qi Hu, Yuan-Mao Hung, Li-Han Chen, Liang-Chuan Lai, Min-Hsiung Pan, Eric Y. Chuang, Mong-Hsun Tsai
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BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2024-01-03 Fernando Cruz, João Capela, Eugénio C. Ferreira, Miguel Rocha, Oscar Dias
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A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-21 Yuerui Liu, Yongquan Jiang, Fan Zhang, Yan Yang
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgraph encoder, a feature encoder and a global feature processor, and a graph augmentation strategy is adopted. The subgraph encoder is responsible for extracting
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BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-21 Junchang Xin, Mingcan Wang, Luxuan Qu, Qi Chen, Weiyiqi Wang, Zhiqiong Wang
Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ${Score}_{Lasso}$
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Genomic Machine Learning Meta-regression: Insights on Associations of Study Features With Reported Model Performance IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-18 Eric J. Barnett, Daniel G. Onete, Asif Salekin, Stephen V. Faraone
Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and
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MiniDBG: A Novel and Minimal De Bruijn Graph for Read Mapping IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-07 Changyong Yu, Yuhai Zhao, Chu Zhao, Jianyu Jin, Keming Mao, Guoren Wang
The De Bruijn graph (DBG) has been widely used in the algorithms for indexing or organizing read and reference sequences in bioinformatics. However, a DBG model that can locate each node, edge and path on sequence has not been proposed so far. Recently, DBG has been used for representing reference sequences in read mapping tasks. In this process, it is not a one-to-one correspondence between the paths
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ENLIGHTENMENT: A Scalable Annotated Database of Genomics and NGS-Based Nucleotide Level Profiles IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-06 Rituparna Sinha, Rajat Kumar Pal, Rajat K. De
The revolution in sequencing technologies has enabled human genomes to be sequenced at a very low cost and time leading to exponential growth in the availability of whole-genome sequences. However, the complete understanding of our genome and its association with cancer is a far way to go. Researchers are striving hard to detect new variants and find their association with diseases, which further gives
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Prediction of Drug–Disease Associations Based on Multi-Kernel Deep Learning Method in Heterogeneous Graph Embedding IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-05 Dandan Li, Zhen Xiao, Han Sun, Xingpeng Jiang, Weizhong Zhao, Xianjun Shen
Computational drug repositioning can identify potential associations between drugs and diseases. This technology has been shown to be effective in accelerating drug development and reducing experimental costs. Although there has been plenty of research for this task, existing methods are deficient in utilizing complex relationships among biological entities, which may not be conducive to subsequent
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SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-05 Wei Wang, Mengxue Yu, Bin Sun, Juntao Li, Dong Liu, Hongjun Zhang, Xianfang Wang, Yun Zhou
Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network
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promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-12-05 Bindi M. Nagda, Van Minh Nguyen, Ryan T. White
Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promoter core regions in the DNA sequence. Detection of promoter regions is necessary if we are to build genetic regulatory networks for biomedical and clinical
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A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-28 Shengpeng Yu, Hong Wang, Jing Li, Jun Zhao, Cheng Liang, Yanshen Sun
MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA),
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CodonU: A Python Package for Codon Usage Analysis IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-28 Souradipto Choudhuri, Keya Sau
Codon Usage Analysis (CUA) has been accompanied by several web servers and independent programs written in several programming languages. Also this diversity speaks for the need of a reusable software that can be helpful in reading, manipulating and acting as a pipeline for such data and file formats. This kind of analyses use multiple tools to address the multifaceted aspects of CUA. So, we propose
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MVDINET: A Novel Multi-Level Enzyme Function Predictor With Multi-View Deep Interactive Learning IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-28 Wenliang Tang, Zhaohong Deng, Hanwen Zhou, Wei Zhang, Fuping Hu, Kup-Sze Choi, Shitong Wang
As a class of extremely significant of biocatalysts, enzymes play an important role in the process of biological reproduction and metabolism. Therefore, the prediction of enzyme function is of great significance in biomedicine fields. Recently, computational methods for predicting enzyme function have been proposed, and they effectively reduce the cost of enzyme function prediction. However, there
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Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-28 Nicolò Rossi, Nicola Gigante, Nicola Vitacolonna, Carla Piazza
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance
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The Exact Stochastic Process of the Haploid Multi-Allelic Wright-Fisher Mutation Model IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-27 Jonas Kristiansen Nøland, Steinar Thorvaldsen
Diffusion models are widely applied in population genetics, but their approximate solutions may not accurately capture the exact stochastic process. Nevertheless, this practice was necessary due to computing limitations, particularly for large populations. In this article, we develop the exact Markov chain algebra (MCA) for a discrete haploid multi-allelic Wright-Fisher model (MA-WFM) with a full mutation
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Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-11-22 Rui Guo, Xu Tian, Hanhe Lin, Stephen McKenna, Hong-Dong Li, Fei Guo, Jin Liu
Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging
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An Event-Driven Approach to Genotype Imputation on a Custom RISC-V Cluster IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-30 Jordan Morris, Ashur Rafiev, Graeme M. Bragg, Mark L. Vousden, David B. Thomas, Alex Yakovlev, Andrew D. Brown
This article proposes an event-driven solution to genotype imputation, a technique used to statistically infer missing genetic markers in DNA. The work implements the widely accepted Li and Stephens model, primary contributor to the computational complexity of modern x86 solutions, in an attempt to determine whether further investigation of the application is warranted in the event-driven domain. The
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AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-27 Aryan Tajmir Riahi, Geoffrey Woollard, Frédéric Poitevin, Anne Condon, Khanh Dao Duc
Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT , which relies on the use of computational optimal transport (OT) to align EM maps in 3D
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An End-to-End Deep Hybrid Autoencoder Based Method for Single-Cell RNA-Seq Data Analysis IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-27 Cunmei Ji, Ning Yu, Yutian Wang, Rong Qi, Chunhou Zheng
Single-cell RNA sequencing technology provides powerful support for researchers to understand the complex mechanisms of cells at the single-cell level. Due to the high sparsity, technical noise, and computational complexity of single-cell transcriptome data, the existing data analysis methods are unable to effectively extract the fine-grained characteristics of scRNA-seq data, resulting in inaccurately
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A Hybrid Heuristic-Exact Optimization for Large-Scale Home Health Care Problem. IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-25 Xiaomin Zhu,Mingyin Zou,Daqian Liu,Ji Wang,Jun Tang,Weidong Bao
During the COVID-19 pandemic, numerous people experiencing illness or senescence choose to receive home health care (HHC) services. However, a rapid increase in patients makes it a challenge to reasonably allocate nurses to provide HHC services under the condition of a paucity of nurse resources and patient time window constraints. To solve the large-scale HHC problem, a hybrid heuristic-exact optimization
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Adaptive Transfer of Graph Neural Networks for Few-Shot Molecular Property Prediction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-25 Baoquan Zhang, Chuyao Luo, Hao Jiang, Shanshan Feng, Xutao Li, Bowen Zhang, Yunming Ye
Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery, which aims to learn transferable knowledge from base property prediction tasks with sufficient data for predicting novel properties with few labeled molecules. Its key challenge is how to alleviate the data scarcity issue of novel properties. Pretrained Graph Neural Network (GNN) based FSMPP methods effectively address
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T-MGCL: Molecule Graph Contrastive Learning Based on Transformer for Molecular Property Prediction IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-19 Xiaoyu Guan, Daoqiang Zhang
In recent years, machine learning has gained increasing traction in the study of molecules, enabling researchers to tackle challenging tasks including molecular property prediction and drug design.Consequently, there remains an open challenge to develop a neural network architecture that can make use of extensive amounts of unlabeled data for training while still providing competitive results in various
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Fault Detectability of Asynchronous Switched Boolean Networks: A Set Reachability Approach IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-19 Liyun Tong, Jinling Liang, Hong-Xiang Hu
This article addresses the fault detectability problem of asynchronous switched Boolean networks, which is focused on whether occurrence of the faults would have an impact on the outputs of the considered network. By applying the semi-tensor product method, the asynchronous switching scheme of the considered system is converted into multiple switching signals. Based on them, an augmented system is
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Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, a Case Study on the Molecular Docking Tool AutoDock Vina IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-13 Xingxing Zhou, Ming Ling, Qingde Lin, Shidi Tang, Jiansheng Wu, Haifeng Hu
Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel Simulated Annealing (pSA) methods have been proposed, where the increase of searching threads can boost the speed of convergence. Although satisfactory solutions can be obtained by these methods, there is no rigorous mathematical analyses on their effectiveness. Thus
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Subgroup Identification Using Virtual Twins for Human Microbiome Studies IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-13 Hyunwook Koh
Even when the same treatment is employed, some patients are cured, while others are not. The patients that are cured may have beneficial microbes in their body that can boost treatment effects, but it is vice versa for the patients that are not cured. That is, treatment effects can vary depending on the patient's microbiome. If the effects of candidate treatments are well-predicted based on the patient's
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Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-10 Yuguang Li, Shuai Lu, Qiang Ma, Xiaofei Nan, Shoutao Zhang
Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of computational models for PPIs prediction have been developed because experimental methods are slow and expensive. Most models employ a sliding window approach
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Bioactive Peptide Recognition Based on NLP Pre-Train Algorithm IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-10 Likun Jiang, Nan Sun, Yue Zhang, Xinyu Yu, Xiangrong Liu
Bioactive peptides are defined as peptide sequences within a protein that can regulate important bodily functions through their myriad activities. With the development of machine learning, more computational methods were proposed for bioactive peptides recognition so that this task does not only rely on tedious and time-consuming wet-experiment. But the training and testing process of existing models
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Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-09 Xue Liu, Qian Cheng, Dan Sun, Wei Wei, Zhiming Zheng
Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint Anonymous-FP that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom
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Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-09 Lijia Ma, Wenwei Deng, Yuan Bai, Zhanwei Du, Minfeng Xiao, Lin Wang, Jianqiang Li, Asoke K. Nandi
Phages are the functional viruses that infect bacteria and they play important roles in microbial communities and ecosystems. Phage research has attracted great attention due to the wide applications of phage therapy in treating bacterial infection in recent years. Metagenomics sequencing technique can sequence microbial communities directly from an environmental sample. Identifying phage sequences
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Gene Expression and Metadata Based Identification of Key Genes for Hepatocellular Carcinoma Using Machine Learning and Statistical Models IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-09 Md. Al Mehedi Hasan, Md. Maniruzzaman, Jungpil Shin
Biomarkers associated with hepatocellular carcinoma (HCC) are of great importance to better understand biological response mechanisms to internal or external intervention. The study aimed to identify key candidate genes for HCC using machine learning (ML) and statistics-based bioinformatics models. Differentially expressed genes (DEGs) were identified using limma and then selected their common genes
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Deep Multi-Dictionary Learning for Survival Prediction With Multi-Zoom Histopathological Whole Slide Images IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-10-03 Chao Tu, Denghui Du, Tieyong Zeng, Yu Zhang
Survival prediction based on histopathological whole slide images (WSIs) is of great significance for risk-benefit assessment and clinical decision. However, complex microenvironments and heterogeneous tissue structures in WSIs bring challenges to learning informative prognosis-related representations. Additionally, previous studies mainly focus on modeling using mono-scale WSIs, which commonly ignore
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Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-09-26 Jing-Xing Liu, Dai-Jun Zhang, Jing-Xiu Zhao, Chun-Hou Zheng, Ying-Lian Gao
Single-cell RNA sequencing (scRNA-Seq) technology has emerged as a powerful tool to investigate cellular heterogeneity within tissues, organs, and organisms. One fundamental question pertaining to single-cell gene expression data analysis revolves around the identification of cell types, which constitutes a critical step within the data processing workflow. However, existing methods for cell type identification
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MetNetComp: Database for Minimal and Maximal Gene-Deletion Strategies for Growth-Coupled Production of Genome-Scale Metabolic Networks IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-09-22 Takeyuki Tamura
Growth-coupled production, in which cell growth forces the production of target metabolites, plays an essential role in the production of substances by microorganisms. The strains are first designed using computational simulation and then validated by biological experiments. In the simulations, gene-deletion strategies are often necessary because many metabolites are not produced in the natural state
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Improving Clinical Decision Making with a Two-Stage Recommender System. IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-09-22 Shaina Raza,Chen Ding
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses challenges due to the multifaceted nature of clinical data and the demand for tailored recommendations. In this paper, we introduce a 2-Stage Recommendation
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Automated Spot Counting in Microbiology IEEE/ACM Trans. Comput. Biol. Bioinform. (IF 4.5) Pub Date : 2023-09-19 Chun-Pang Lin, Yajie Duan, Davit Sargsyan, Javier Cabrera, Christine M. Livingston, Robert Vogel, John Hartman, Mayukh Das, Willem Talloen, Helena Geys, Evangelos D. Kanoulas, Surya Mohanty
Biological samples are routinely analyzed for microbe concentration. The samples are diluted, loaded onto established host cell cultures, and incubated. If infectious agents are present in the samples, they form circular spots that do not contain the host cells. Each spot is assumed to be originated from a single microbial unit such as a bacterial colony forming unit or viral plaque forming unit. The