IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献

MG-TCCA: Tensor Canonical Correlation Analysis Across Multiple Groups. MG-TCCA：跨多组的张量典型相关分析。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-07-01 DOI: 10.1109/TCBB.2024.3471930

Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen

{"title":"MG-TCCA: Tensor Canonical Correlation Analysis Across Multiple Groups.","authors":"Zhuoping Zhou, Boning Tong, Davoud Ataee Tarzanagh, Bojian Hou, Andrew J Saykin, Qi Long, Li Shen","doi":"10.1109/TCBB.2024.3471930","DOIUrl":"10.1109/TCBB.2024.3471930","url":null,"abstract":"Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA and Sparse TCCA (STCCA) in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"1299-1310"},"PeriodicalIF":3.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

NeoMS: Mass Spectrometry-Based Method for Uncovering Mutated MHC-I Neoantigens. NeoMS：基于质谱的发现变异 MHC-I 新抗原的方法。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3447746

Shaokai Wang, Ming Zhu, Bin Ma

{"title":"NeoMS: Mass Spectrometry-Based Method for Uncovering Mutated MHC-I Neoantigens.","authors":"Shaokai Wang, Ming Zhu, Bin Ma","doi":"10.1109/TCBB.2024.3447746","DOIUrl":"10.1109/TCBB.2024.3447746","url":null,"abstract":"Major Histocompatibility Complex (MHC) molecules play a critical role in the immune system by presenting peptides on the cell surface for recognition by T-cells. Tumor cells often produce MHC peptides with amino acid mutations, known as neoantigens, which evade T-cell recognition, leading to rapid tumor growth. In immunotherapies such as TCR-T and CAR-T, identifying these mutated MHC peptide sequences is crucial. Current mass spectrometry-based peptide identification methods primarily rely on database searching, which fails to detect mutated peptides not present in human databases. In this paper, we propose a novel workflow called NeoMS, designed to efficiently identify both non-mutated and mutated MHC-I peptides from mass spectrometry data. NeoMS utilizes a tagging algorithm to generate an expanded sequence database that includes potential mutated proteins for each sample. Furthermore, it employs a machine learning-based scoring function for each peptide-spectrum match (PSM) to maximize search sensitivity. Finally, a rigorous target-decoy approach is implemented to control the false discovery rates (FDR) of the peptides with and without mutations separately. Experimental results for regular peptides demonstrate that NeoMS outperforms four benchmark methods. For mutated peptides, NeoMS successfully identifies hundreds of high-quality mutated peptides in a melanoma-associated sample, with their validity confirmed by further studies.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"444-454"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles. AnglesRefine：利用基于扭转角的变换器完善三维蛋白质结构

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3422288

Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao

{"title":"AnglesRefine: Refinement of 3D Protein Structures Using Transformer Based on Torsion Angles.","authors":"Lei Zhang, Junyong Zhu, Sheng Wang, Jie Hou, Dong Si, Renzhi Cao","doi":"10.1109/TCBB.2024.3422288","DOIUrl":"10.1109/TCBB.2024.3422288","url":null,"abstract":"The goal of protein structure refinement is to enhance the precision of predicted protein models, particularly at the residue level of the local structure. Existing refinement approaches primarily rely on physics, whereas molecular simulation methods are resource-intensive and time-consuming. In this study, we employ deep learning methods to extract structural constraints from protein structure residues to assist in protein structure refinement. We introduce a novel method, AnglesRefine, which focuses on a protein's secondary structure and employs transformer to refine various protein structure angles (psi, phi, omega, CA_C_N_angle, C_N_CA_angle, N_CA_C_angle), ultimately generating a superior protein model based on the refined angles. We evaluate our approach against other cutting-edge methods using the CASP11-14 and CASP15 datasets. Experimental outcomes indicate that our method generally surpasses other techniques on the CASP11-14 test dataset, while performing comparably or marginally better on the CASP15 test dataset. Our method consistently demonstrates the least likelihood of model quality degradation, e.g., the degradation percentage of our method is less than 10%, while other methods are about 50%. Furthermore, as our approach eliminates the need for conformational search and sampling, it significantly reduces computational time compared to existing refinement methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"397-408"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141497925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

GrapHiC: An Integrative Graph Based Approach for Imputing Missing Hi-C Reads. GrapHiC：一种基于图的综合方法，用于估算缺失的 Hi-C 读数。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3477909

Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh

{"title":"GrapHiC: An Integrative Graph Based Approach for Imputing Missing Hi-C Reads.","authors":"Ghulam Murtaza, Justin Wagner, Justin M Zook, Ritambhara Singh","doi":"10.1109/TCBB.2024.3477909","DOIUrl":"10.1109/TCBB.2024.3477909","url":null,"abstract":"Hi-C experiments allow researchers to study and understand the 3D genome organization and its regulatory function. Unfortunately, sequencing costs and technical constraints severely restrict access to high-quality Hi-C data for many cell types. Existing frameworks rely on a sparse Hi-C dataset or cheaper-to-acquire ChIP-seq data to predict Hi-C contact maps with high read coverage. However, these methods fail to generalize to sparse or cross-cell-type inputs because they do not account for the contributions of epigenomic features or the impact of the structural neighborhood in predicting Hi-C reads. We propose GrapHiC, which combines Hi-C and ChIP-seq in a graph representation, allowing more accurate embedding of structural and epigenomic features. Each node represents a binned genomic region, and we assign edge weights using the observed Hi-C reads. Additionally, we embed ChIP-seq and relative positional information as node attributes, allowing our representation to capture structural neighborhoods and the contributions of proteins and their modifications for predicting Hi-C reads. We show that GrapHiC generalizes better than the current state-of-the-art on cross-cell-type settings and sparse Hi-C inputs. Moreover, we can utilize our framework to impute Hi-C reads even when no Hi-C contact map is available, thus making high-quality Hi-C data accessible for many cell types.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"409-419"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142406376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma. 开发并验证用于识别食管鳞状细胞癌免疫相关靶点的竞争性内源性 circRNA/miRNA/mRNA 网络综合分析方法

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3443854

Chu-Ting Yu, Bo Tian, Qian-Qian Meng, Zhe-Ran Chen, Ya-Nan Pang, Xun Zhang, Yan Bian, Si-Wei Zhou, Mei-Juan Hao, Ye Gao, Lei Xin, Han Lin, Wei Wang, Luo-Wei Wang

{"title":"Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma.","authors":"Chu-Ting Yu, Bo Tian, Qian-Qian Meng, Zhe-Ran Chen, Ya-Nan Pang, Xun Zhang, Yan Bian, Si-Wei Zhou, Mei-Juan Hao, Ye Gao, Lei Xin, Han Lin, Wei Wang, Luo-Wei Wang","doi":"10.1109/TCBB.2024.3443854","DOIUrl":"10.1109/TCBB.2024.3443854","url":null,"abstract":"Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"481-492"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

ESGC-MDA: Identifying miRNA-Disease Associations Using Enhanced Simple Graph Convolutional Networks. ESGC-MDA：利用增强型简单图卷积网络识别 miRNA 与疾病的关联。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3486911

Xuehua Bi, Chunyang Jiang, Cheng Yan, Kai Zhao, Linlin Zhang, Jianxin Wang

{"title":"ESGC-MDA: Identifying miRNA-Disease Associations Using Enhanced Simple Graph Convolutional Networks.","authors":"Xuehua Bi, Chunyang Jiang, Cheng Yan, Kai Zhao, Linlin Zhang, Jianxin Wang","doi":"10.1109/TCBB.2024.3486911","DOIUrl":"10.1109/TCBB.2024.3486911","url":null,"abstract":"MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is urgent to develop efficient computational methods for predicting potential miRNA-disease associations to reduce the cost and time associated with biological wet experiments. In addition, high-quality feature representation remains a challenge for miRNA-disease association prediction using graph neural network methods. In this paper, we propose a method named ESGC-MDA, which employs an enhanced Simple Graph Convolution Network to identify miRNA-disease associations. We first construct a bipartite attributed graph for miRNAs and diseases by computing multi-source similarity. Then, we enhance the feature representations of miRNA and disease nodes by applying two strategies in the simple convolution network, which include randomly dropping messages during propagation to ensure the model learns more reliable feature representations, and using adaptive weighting to aggregate features from different layers. Finally, we calculate the prediction scores of miRNA-disease pairs by using a fully connected neural network decoder. We conduct 5-fold cross-validation and 10-fold cross-validation on HDMM v2.0 and HMDD v3.2, respectively, and ESGC-MDA achieves better performance than state-of-the-art baseline methods. The case studies for cardiovascular disease, lung cancer and colon cancer also further confirm the effectiveness of ESGC-MDA.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"422-432"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Partition Based Algorithms for Rearrangement Distances With Flexible Intergenic Regions. 基于分区的灵活基因间重排距离算法

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3467033

Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Geraldine Jean, Guillaume Fertin, Zanoni Dias

{"title":"Partition Based Algorithms for Rearrangement Distances With Flexible Intergenic Regions.","authors":"Gabriel Siqueira, Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Geraldine Jean, Guillaume Fertin, Zanoni Dias","doi":"10.1109/TCBB.2024.3467033","DOIUrl":"10.1109/TCBB.2024.3467033","url":null,"abstract":"Genome Rearrangement distance problems are used in Computational Biology to estimate the evolutionary distance between genomes. These problems consist of minimizing the number of rearrangement events necessary to transform one genome into another. Two commonly used rearrangement events are reversal and transposition. The first studied problems ignored nucleotides outside genes (called intergenic regions), or assumed that genomes have a single copy of each gene. Recent works made advancements in more general problems considering the number of nucleotides in intergenic regions, and replicated genes. Nevertheless, genomes tend to have wildly different quantities of nucleotides on their intergenic regions, which poses a problem when comparing these regions exactly. To overcome this limitation, our work considers some flexibility when matching intergenic regions that do not have the same number of nucleotides. We propose new problems seeking the minimum number of reversals, or reversals and transpositions, necessary to transform one genome into another, while considering flexible intergenic region information. We show approximations for these problems by exploring their relationship with the Signed Minimum Common Flexible Intergenic String Partition problem. We also present different heuristics for the partition problem, and conduct experimental tests on simulated genomes to assess the performance of our algorithms.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"455-468"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142345931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey. 基于人工智能的计算方法在早期药物发现和上市后药物评估中的应用：调查。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3492708

Flora Rajaei, Cristian Minoccheri, Emily Wittrup, Richard C Wilson, Brian D Athey, Gilbert S Omenn, Kayvan Najarian

引用次数: 0

Detecting Boolean Asymmetric Relationships With a Loop Counting Technique and its Implications for Analyzing Heterogeneity Within Gene Expression Datasets. 利用循环计数技术检测布尔不对称关系及其对分析基因表达数据集异质性的影响

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3487434

Haosheng Zhou, Wei Lin, Sergio R Labra, Stuart A Lipton, Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan

{"title":"Detecting Boolean Asymmetric Relationships With a Loop Counting Technique and its Implications for Analyzing Heterogeneity Within Gene Expression Datasets.","authors":"Haosheng Zhou, Wei Lin, Sergio R Labra, Stuart A Lipton, Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan","doi":"10.1109/TCBB.2024.3487434","DOIUrl":"10.1109/TCBB.2024.3487434","url":null,"abstract":"Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of 'symmetric' relationship. Biclustering is one such technique that typically searches for subsets of genes exhibiting correlated expression among a subset of samples. However, genes can also exhibit 'asymmetric' relationships, such as 'if-then' relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these 'boolean-asymmetric' relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, e.g., reflecting regulatory effects of one gene on another, rather than more standard symmetric interactions. Unlike typical approaches that search for BARs across the entire population, BAR-biclusters can detect asymmetric interactions that only occur among a subset of samples. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BAR-biclusters indeed contain additional information not present within the more traditional 'boolean-symmetric'-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetric-signals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"27-38"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Performance Comparison Between Deep Neural Network and Machine Learning Based Classifiers for Huntington Disease Prediction From Human DNA Sequence. 基于深度神经网络和机器学习的分类器在从人类 DNA 序列预测亨廷顿病方面的性能比较。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3493203

C Vishnuppriya, G Tamilpavai

{"title":"Performance Comparison Between Deep Neural Network and Machine Learning Based Classifiers for Huntington Disease Prediction From Human DNA Sequence.","authors":"C Vishnuppriya, G Tamilpavai","doi":"10.1109/TCBB.2024.3493203","DOIUrl":"10.1109/TCBB.2024.3493203","url":null,"abstract":"Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"52-63"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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