IEEE transactions on computational biology and bioinformatics最新文献_第10页

TransDNA: A Deep Transfer Learning Network for Sequence Reconstruction in DNA-Based Data Storage. TransDNA：基于dna的数据存储中序列重建的深度迁移学习网络。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-26 DOI: 10.1109/TCBBIO.2025.3602912

Yun Qin, Fei Zhu, Bo Xi, Yuping Duan

{"title":"TransDNA: A Deep Transfer Learning Network for Sequence Reconstruction in DNA-Based Data Storage.","authors":"Yun Qin, Fei Zhu, Bo Xi, Yuping Duan","doi":"10.1109/TCBBIO.2025.3602912","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3602912","url":null,"abstract":"DNA is a promising storage medium, offering advantages in high density, long durability, and low maintenance cost. However, information recovery in DNA storage systems is challenged by errors arising during synthesis, amplification, and sequencing phases. A key challenge in decoding is sequence reconstruction, which involves recovering the original reference sequence from a set of noisy copies. While recent research has explored deep learning-based methods for this task, the high cost of synthesis and sequencing results in a limited availability of training samples. To overcome this challenge, we propose TransDNA, a deep transfer learning network specifically designed for sequence reconstruction in DNA storage. It consists of an encoder, a domain-specific decoder, and a domain-invariant feature extractor, with alternating domain alignment and domain-specific reconstruction mechanisms. By transferring knowledge from a larger source dataset, TransDNA significantly enhances the reconstruction success rate on two target datasets from real DNA storage experiments, outperforming the base model without transfer learning and several comparative methods. Notably, TransDNA surpasses the SDG method in both reconstruction success rate and training efficiency. These results demonstrate the effectiveness of TransDNA as the first transfer learning approach applied to the DNA sequence reconstruction task. The source code is available at: https://github.com/qinyunnn/TransDNA.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nature-Inspired Meta-Heuristic Algorithms for Detecting Protein Complexes in Protein-Protein Interaction Networks: A Survey. 在蛋白质-蛋白质相互作用网络中检测蛋白质复合物的自然启发的元启发式算法：综述。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-25 DOI: 10.1109/TCBBIO.2025.3602120

Wei Zheng, Jianyong Sun, Haotian Zhang

{"title":"Nature-Inspired Meta-Heuristic Algorithms for Detecting Protein Complexes in Protein-Protein Interaction Networks: A Survey.","authors":"Wei Zheng, Jianyong Sun, Haotian Zhang","doi":"10.1109/TCBBIO.2025.3602120","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3602120","url":null,"abstract":"In cells, various proteins interact to form protein-protein interaction networks. Protein complexes serve as the fundamental units that enable essential functions in biological organisms. Directly detecting protein complexes in protein-protein interaction networks using biology-based methods is costly and time-consuming. In recent years, different computational methods have emerged for identifying or predicting protein complexes within these networks. Following a comprehensive analysis of the issue, the optimization algorithm based on meta-heuristic methods has been completely executed for this specific type of challenge, which started in 2004. This paper provides a systematic survey of such meta-heuristic algorithms. Specifically, 34 related methods published between 2004 and 2024 are collected and summarized, focusing on how to model problems and design optimization algorithms, which are the key components of this survey. Our observations and analyses have revealed several limitations in existing studies and suggested potential avenues for future research.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

scDVAE:Single-Cell Data Clustering Based on Variational Autoencoder with Disentangled Latent Representations. scDVAE：基于无纠缠潜表示变分自编码器的单细胞数据聚类。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-14 DOI: 10.1109/TCBBIO.2025.3599194

Xiaohan Zou, Weihua Zheng, Shunfang Wang

{"title":"scDVAE:Single-Cell Data Clustering Based on Variational Autoencoder with Disentangled Latent Representations.","authors":"Xiaohan Zou, Weihua Zheng, Shunfang Wang","doi":"10.1109/TCBBIO.2025.3599194","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3599194","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) technology enables the analysis of gene expression in individual cells, allowing for a deeper exploration of heterogeneity in organisms and complex diseases. Cell clustering is a crucial step in singlecell analysis, enabling the identification of cellular heterogeneity. However, the high dimensionality, sparsity, and dropout events in single-cell data have brought enormous challenges to clustering analysis. Building on the proven success of deep generative models in learning meaningful representations from lowdimensional latent spaces, we introduce scDVAE, a novel deep generative approach that leverages a variational autoencoder with disentangled latent representations for single-cell clustering. Firstly, each latent representation generated by the encoder is disentangled into clustering features and generative features. In this way, the clustering features can enhance the performance of the clustering task without interference from the generative task. Secondly, we employ a Student's t-mixture model as the prior distribution for the clustering features to enhance the robustness of our method against dropout events. In addition, we introduce a hybrid data augmentation strategy to generate augmented scRNA-seq data, which enhances dataset diversity while also helping to reduce noise. Our experimental studies on 10 realworld datasets demonstrate that scDVAE significantly improves clustering performance compared to state-of-the-art methods.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Kernel-Based Hilbert Space Framework for Predictive Modeling of lncRNA-miRNA-Disease Interaction Networks. lncrna - mirna -疾病相互作用网络预测建模的新型核基Hilbert空间框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-12 DOI: 10.1109/TCBBIO.2025.3598013

Shivani Saxena, Ahsan Z Rizvi

{"title":"A Novel Kernel-Based Hilbert Space Framework for Predictive Modeling of lncRNA-miRNA-Disease Interaction Networks.","authors":"Shivani Saxena, Ahsan Z Rizvi","doi":"10.1109/TCBBIO.2025.3598013","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3598013","url":null,"abstract":"Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are vital regulators of gene expression. They are closely linked to the development and progression of various diseases, including cancer and neurodegenerative disorders. Their interactions form complex lncRNA-miRNA-disease networks (LMDNets), which modulate key disease-associated biological pathways. Although recent computational approaches have attempted to integrate diverse biological data such as gene expression profiles, sequence information, and disease associations. They often face significant challenges, including limited interpretability, reliance on manually curated datasets, poor scalability, and high sensitivity to noise and missing data. To overcome these limitations, we propose KHSF-LMDNet, a kernel-based Hilbert space framework for modeling lncRNA-miRNA-disease networks. This model integrates graph-based biological networks, similarity features, and deep learning with an attention mechanism to map complex interactions into a Hilbert subspace, enabling more robust and interpretable learning. Evaluated on benchmark datasets, KHSF-LMDNet consistently outperforms existing methods in terms of accuracy, precision, and area under the ROC curve (AUC). Additionally, it effectively ranks disease-associated lncRNAs and miRNAs, identifying top candidates related to cancer and Alzheimer's disease, thereby supporting functional genomics research and facilitating the discovery of novel biomarkers for precision medicine.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fragment-Driven Progressive Alternating Diffusion for De Novo Molecular Design. 片段驱动的渐进式交替扩散新分子设计。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-12 DOI: 10.1109/TCBBIO.2025.3598112

Xing Cai, Tong Zhang, Yide Qiu, Zhen Cui

{"title":"Fragment-Driven Progressive Alternating Diffusion for De Novo Molecular Design.","authors":"Xing Cai, Tong Zhang, Yide Qiu, Zhen Cui","doi":"10.1109/TCBBIO.2025.3598112","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3598112","url":null,"abstract":"High reliability and creativity remain key goals for AI-driven de novo molecule design. In this work, we propose a fragment-driven progressive alternating diffusion (FDPAD) framework in a coarse-to-fine generation mode. By modeling molecules as fragment-structured graphs, FDPAD entails a progressive discrete diffusion process by randomly walking some sequences of fragment-structured units (FSU), thereby mitigating combinatorial complexities and facilitating the synthesis of intricate macroscopic structures. To delve deeper internal structures of FSU, we design two distinct diffusion processes: the conditioned fragment diffusion (CFD) and the inter-fragment bond diffusion (IBD). In CFD, a string-based diffusion probability model is proposed to enrich the diversity of fragments, leveraging the partially-generated molecule as condition. And in IBD, a graph-based diffusion model upon bond-related atom graph is proposed to boost the prediction of intricate chemical bond connections among molecular fragments. Through the interleaving of CFD and IBD processes, our model outperforms state-of-the-art algorithms in de novo molecular generation, particularly in generating novel and unique molecules.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Framework for Protein-to-Metal Binding Prediction Using Protein Language Models. 基于蛋白质语言模型的蛋白质与金属结合预测的深度学习框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-11 DOI: 10.1109/TCBBIO.2025.3595446

Fairuz Shadmani Shishir, Bishnu Sarker, Farzana Rahman, Sumaiya Shomaji

{"title":"A Deep Learning Framework for Protein-to-Metal Binding Prediction Using Protein Language Models.","authors":"Fairuz Shadmani Shishir, Bishnu Sarker, Farzana Rahman, Sumaiya Shomaji","doi":"10.1109/TCBBIO.2025.3595446","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3595446","url":null,"abstract":"This study presents an end-to-end deep learning framework for protein-to-metal-ion binding prediction, a critical task in understanding protein function, structural stability, and metal transport mechanisms. A binding site is a residue location in a protein sequence where a metal binds to a protein. Manual curation of metal binding sites is a tedious process involving mining through research articles, making it expensive, laborious, and time-consuming. Therefore, developing a computational pipeline is essential to predict metal ion binding of unannotated proteins. A significant shortcoming of existing computational methods is the failure to capture the long-term dependency of the residues, the absence of positional information, and a pre-determined set of residues and metal ions. In this paper, we propose a metal-ion binding prediction pipeline using a large language model, emphasizing 1) the comparative performance of five state-of-the-art protein language models (pLMs), 2) the impact of positional encoding of binding sites, and 3) the comparison with classical machine learning techniques. A 10-fold cross-validation evaluation yielded a Matthews Correlation Coefficient (MCC) of 0.89, along with precision, recall, and F1 scores exceeding 95% for the six most extensively studied metal ions reported in the literature.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A More Efficient Dynamic Programming Algorithm for Designing a Coding Sequence by Jointly Optimizing Its Structural Stability and Codon Usage. 一种更高效的编码序列结构稳定性和密码子使用优化动态规划算法。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-07 DOI: 10.1109/TCBBIO.2025.3596771

Yan-Ru Ju, Long-Shang Cho, Chin Lung Lu

{"title":"A More Efficient Dynamic Programming Algorithm for Designing a Coding Sequence by Jointly Optimizing Its Structural Stability and Codon Usage.","authors":"Yan-Ru Ju, Long-Shang Cho, Chin Lung Lu","doi":"10.1109/TCBBIO.2025.3596771","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3596771","url":null,"abstract":"Currently, a dynamic programming (DP) algorithm CDSfold has been proposed to design a CDS by minimizing the minimum free energy (MFE) of its secondary structure. However, it has been questioned recently that such a DP algorithm is difficult to be modified to design a CDS when attempting to jointly optimize its secondary structure stability and codon adaptation index (CAI). In this study, we successfully modify the DP algorithm of CDSfold to exactly solve this kind of CDS design problem in $mathcal {O}(L^{3})$ time and $mathcal {O}(L^{2})$ space, where $L$ is the CDS length. We further accelerate this DP algorithm by beam search, enabling it to design a high-quality approximate CDS in $mathcal {O}(L)$ time, and implement it as the program LinearCDSfold. Our experimental results show that when running with exact search, LinearCDSfold has comparable accuracy to two state-of-the-art CDS design tools LinearDesign and DERNA in terms of both MFE and CAI. In terms of running time, however, LinearCDSfold is slower than LinearDesign, but significantly faster than DERNA, even though they all run in $mathcal {O}(L^{3})$ time and $mathcal {O}(L^{2})$ space. Moreover, LinearCDSfold using beam search can design an approximate CDS in very short time with very high quality in terms of both MFE and CAI.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Deep Learning Framework for Enhanced Melanoma Detection. 增强黑色素瘤检测的混合深度学习框架。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-07 DOI: 10.1109/TCBBIO.2025.3596589

Peng Zhang, Divya Chaudhary

{"title":"Hybrid Deep Learning Framework for Enhanced Melanoma Detection.","authors":"Peng Zhang, Divya Chaudhary","doi":"10.1109/TCBBIO.2025.3596589","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3596589","url":null,"abstract":"Cancer remains a leading cause of death worldwide, underscoring the urgent need for advancements in early detection and treatment technologies. In this paper, we propose the SegFusion Framework, a novel and highly efficient hybrid approach for melanoma detection, combining the segmentation power of U-Net with the classification strength of EfficientNet. The primary objective of our study is to improve the accuracy and efficiency of melanoma detection through this innovative integration. To achieve this, we utilized the HAM10000 dataset to train the U-Net model for accurate segmentation of cancerous regions. Simultaneously, the ISIC 2020 dataset was employed to train the EfficientNet model, optimizing it for binary classification of skin cancer. The proposed SegFusion Framework significantly outperforms existing models, achieving an accuracy of 99.01% on the ISIC 2020 dataset, along with a precision of 0.99, recall of 0.99, F1 score of 0.99, and a MCC of 0.97. Additionally, we conducted experiments comparing our approach with the advanced SkinViT model and other recent hybrid methods. The SkinViT model, trained on the same dataset, achieved an accuracy of 98.20%, which is notably lower than that of the SegFusion Framework, further highlighting the superiority of our method. By leveraging U-Net's precise segmentation capabilities and EfficientNet's robust classification performance, the SegFusion Framework offers a comprehensive solution for melanoma detection. Our extensive experiments demonstrate the high accuracy and reliability of our method in both segmentation and classification tasks, marking a significant step forward in automated skin cancer detection. We believe that our framework sets a new benchmark in the field, providing a reliable tool for medical professionals in the early diagnosis and treatment of melanoma and fostering further research in medical imaging.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GPFN: Prior-Data Fitted Networks for Genomic Prediction. 基因组预测的先验数据拟合网络。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-07 DOI: 10.1109/TCBBIO.2025.3596744

Jordan Ubbens, Ian Stavness, Andrew G Sharpe

{"title":"GPFN: Prior-Data Fitted Networks for Genomic Prediction.","authors":"Jordan Ubbens, Ian Stavness, Andrew G Sharpe","doi":"10.1109/TCBBIO.2025.3596744","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3596744","url":null,"abstract":"Genomic Prediction (GP) methods predict the breeding value of unphenotyped individuals in order to select parental candidates breeding populations of livestock and crop plants. Among models for GP, classical linear models have remained consistently popular, while more complex nonlinear methods such as deep neural networks have shown comparable accuracy at best. In this work, we propose the Genomic Prior-Data Fitted Network (GPFN) as a new paradigm for GP. GPFNs perform amortized Bayesian inference by simulating hundreds of thousands or millions of plant or animal populations. This allows GPFNs to be deployed without requiring any training or tuning, providing predictions in a single inference pass. On three populations of plants across two different crop species, GPFNs perform significantly better than the linear baseline on 13 out of 16 traits. On a challenging between-families structured prediction task on a third crop species, the GPFN matches the performance of the linear baseline while outperforming it in one location. GPFNs represent a completely new direction for the field of genomic prediction, and have the potential to unlock levels of selection accuracy not possible with existing methods, especially in diverse populations.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LRTM: Left-Right Transition Matrices for Molecular Association Prediction. LRTM：用于分子关联预测的左右转移矩阵。

IEEE transactions on computational biology and bioinformatics Pub Date : 2025-08-06 DOI: 10.1109/TCBBIO.2025.3596306

Kai Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang

{"title":"LRTM: Left-Right Transition Matrices for Molecular Association Prediction.","authors":"Kai Zheng, Guihua Duan, Mengyun Yang, Wei Wu, Yao-Hang Li, Jianxin Wang","doi":"10.1109/TCBBIO.2025.3596306","DOIUrl":"https://doi.org/10.1109/TCBBIO.2025.3596306","url":null,"abstract":"molecular associations are central to most biological processes. The discovery and identification of potential associations between molecules can provide insights into biological exploration, diagnostic and therapeutic interventions, and drug development. So far many relevant computational methods have been proposed, but most of them are usually limited to specific domains and rely on complex preprocessing procedures, which restricts the models' ability to be applied to other tasks. Therefore, it remains a challenge to explore a generalized approach to accurately predicting potential associations. In this study, We propose Left-Right Transition Matrices (LRTM) for molecular association prediction. From the perspective on the diffusion model, we construct two transition matrices to model undirected graph information propagation. This allows modeling the transition probabilities of links, which facilitates link prediction in molecular bipartite networks. The extensive experimental results show that the proposed LRTM algorithm performs better than the compared methods. Also, the proposed algorithm has the potential for cross-task prediction. Furthermore, case studies show that LRTM is a powerful tool that can be effectively applied to practical applications.","PeriodicalId":520987,"journal":{"name":"IEEE transactions on computational biology and bioinformatics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144857611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0