| Выпуск | Название | Файл | 
		| Том 19, № 4 (2024) | A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction |  (Eng)
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	| Wei M., Yu C., Li L., You Z., Lei-Wang . | 
		| Том 19, № 6 (2024) | A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data |  (Eng)
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	| Peng X., Cui W., Zhang W., Li Z., Zhu X., Yuan L., Li J. | 
		| Том 19, № 5 (2024) | A Novel In silico Filtration Method for Discovery of Encrypted Antimicrobial Peptides |  (Eng)
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	| Barneh F., Nazarian A., Mousavi Nadoshan R., Pooshang Bagheri K. | 
		| Том 19, № 8 (2024) | A Novel Natural Graph for Efficient Clustering of Virus Genome Sequences |  (Eng)
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	| Song H., Sun N., Yu W., Yau S. | 
		| Том 19, № 6 (2024) | A Review of Drug-related Associations Prediction Based on Artificial Intelligence Methods |  (Eng)
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	| Ma M., Lei X., Zhang Y. | 
		| Том 19, № 6 (2024) | A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction |  (Eng)
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	| Kaur I., Ahmad T., Doja M. | 
		| Том 19, № 3 (2024) | A Systematic Review of the Application of Machine Learning in CpG Island (CGI) Detection and Methylation Prediction |  (Eng)
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	| Wei R., Zhang L., Zheng H., Xiao M. | 
		| Том 19, № 3 (2024) | A Unified Probabilistic Framework for Modeling and Inferring Spatial Transcriptomic Data |  (Eng)
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	| Huang Z., Luo S., Zhang Z., Wang Z., Zhou T., Zhang J. | 
		| Том 19, № 3 (2024) | Advancements in Yoga Pose Estimation Using Artificial Intelligence: A Survey |  (Eng)
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	| Chamola V., Gummana E., Madan A., Rout B., Coelho Rodrigues J. | 
		| Том 19, № 10 (2024) | Advances in Deep Learning Assisted Drug Discovery Methods: A Self-review |  (Eng)
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	| Zhang H., Saravanan K. | 
		| Том 19, № 7 (2024) | An Explainable Multichannel Model for COVID-19 Time Series Prediction |  (Eng)
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	| He H., Xie J., Lu X., Huang D., Zhang W. | 
		| Том 19, № 9 (2024) | Application of Deep Learning Neural Networks in Computer-Aided Drug Discovery: A Review |  (Eng)
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	| Mathivanan J., Dhayabaran V., David M., Karuna Nidhi M., Prasath K., Suvaithenamudhan S. | 
		| Том 19, № 3 (2024) | Application of Improved Support Vector Machine for Pulmonary Syndrome Exposure with Computer Vision Measures |  (Eng)
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	| Khadidos A., Alshareef A., Manoharan H., Khadidos A., Selvarajan S. | 
		| Том 19, № 10 (2024) | A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins |  (Eng)
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	| Lei C., Fang S., Li Y., Guo F., Li M. | 
		| Том 19, № 6 (2024) | Bioinformatic Resources for Plant Genomic Research |  (Eng)
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	| Valsala Sudarsanan S., Sreekumar N. | 
		| Том 19, № 4 (2024) | Bioinformatics Perspective of Drug Repurposing |  (Eng)
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	| Patel B., Gelat B., Soni M., Rathaur P., SR K. | 
		| Том 19, № 10 (2024) | CFCN: An HLA-peptide Prediction Model based on Taylor Extension Theory and Multi-view Learning |  (Eng)
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	| Rao B., Han B., Wei L., Zhang Z., Jiang X., Manavalan B. | 
		| Том 19, № 1 (2024) | Computational Methods for Functional Characterization of lncRNAS in Human Diseases: A Focus on Co-Expression Networks |  (Eng)
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	| Jha P., Barbeiro M., Lupieri A., Aikawa E., Uchida S., Aikawa M. | 
		| Том 19, № 3 (2024) | Deep Learning for Clustering Single-cell RNA-seq Data |  (Eng)
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	| Zhu Y., Bai L., Ning Z., Fu W., Liu J., Jiang L., Fei S., Gong S., Lu L., Deng M., Yi M. | 
		| Том 19, № 9 (2024) | Deep Learning in DNA- and RNA-sequence Analysis: Advances and New Challenges |  (Eng)
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	| Li X. | 
		| Том 19, № 7 (2024) | DeepEpi: Deep Learning Model for Predicting Gene Expression Regulation Based on Epigenetic Histone Modifications |  (Eng)
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	| Hamdy R., Omar Y., Maghraby F. | 
		| Том 19, № 9 (2024) | DeepPTM: Protein Post-translational Modification Prediction from Protein Sequences by Combining Deep Protein Language Model with Vision Transformers |  (Eng)
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	| Soylu N., Sefer E. | 
		| Том 19, № 7 (2024) | Discovering Microbe-disease Associations with Weighted Graph Convolution Networks and Taxonomy Common Tree |  (Eng)
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	| Xing J., Shi Y., Su X., Wu S. | 
		| Том 19, № 5 (2024) | DMR_Kmeans: Identifying Differentially Methylated Regions Based on k-means Clustering and Read Methylation Haplotype Filtering |  (Eng)
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	| Peng X., Cui W., Kong X., Huang Y., Li J. | 
		| Том 19, № 4 (2024) | Drug-target Interaction Prediction By Combining Transformer and Graph Neural Networks |  (Eng)
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	| Liu J., Lu Y., Guan S., Jiang T., Ding Y., Fu Q., Cui Z., Wu H. | 
		| Том 19, № 10 (2024) | Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration |  (Eng)
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	| Zhang R., Zhu B., Jiang T., Cui Z., Wu H. | 
		| Том 19, № 9 (2024) | FMDVSerPred: A Novel Computational Solution for Foot-and-mouth Disease Virus Classification and Serotype Prediction Prevalent in Asia Using VP1 Nucleotide Sequence Data |  (Eng)
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	| Das S., Pal S., Mahapatra S., Biswal J., Pradhan S., Sahoo A., Singh R. | 
		| Том 19, № 5 (2024) | Full-length PacBio Amplicon Sequencing to Unveil RNA Editing Sites |  (Eng)
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	| Zhu X., Liao M., Zhu Y., Dong Y. | 
		| Том 19, № 10 (2024) | Genotype and Phenotype Association Analysis Based on Multi-omics Statistical Data |  (Eng)
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	| Guo X., Song Y., Xu D., Jin X., Shang X. | 
		| Том 19, № 1 (2024) | Identification and Functional Prediction of lncRNAs using Bioinformatic Techniques |  (Eng)
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	| Uchida S. | 
		| Том 19, № 8 (2024) | Identification of Mitophagy-Related Genes in Sepsis |  (Eng)
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	| Zeng X., Zhang M., Liao S., Wang Y., Ren Y., Li R., Li T., Mao A., Li G., Zhang Y. | 
		| Том 19, № 8 (2024) | Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm |  (Eng)
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	| Cui Y., Wei L., Wang R., Ye X., Sakurai T. | 
		| Том 19, № 4 (2024) | Identifying Pathological Myopia Associated Genes with A Random Walk-Based Method in Protein-Protein Interaction Network |  (Eng)
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	| Zhang J., Huang T., Sun Q., Zhang J. | 
		| Том 19, № 2 (2024) | In silico Study of Clinical Prognosis Associated MicroRNAs for Patients with Metastasis in Clear Cell Renal Carcinoma |  (Eng)
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	| Wijaya E., Mekala V., Zaenudin E., Ng K. | 
		| Том 19, № 8 (2024) | Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks |  (Eng)
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	| Tan D., Wang J., Cheng Z., Su Y., Zheng C. | 
		| Том 19, № 10 (2024) | Integrated Machine Learning Algorithms for Stratification of Patients with Bladder Cancer |  (Eng)
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	| He Y., Wei H., Liao S., Ou R., Xiong Y., Zuo Y., Yang L. | 
		| Том 19, № 4 (2024) | Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma |  (Eng)
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	| Zhu X., Wang X., Wang H., Xiao Y., Jiang M., Wang M., Zhang N., Xie A., Yuan H., Zhang Z., Zhang J., Xu Y. | 
		| Том 19, № 9 (2024) | Integration of Artificial Intelligence, Machine Learning and Deep Learning Techniques in Genomics: Review on Computational Perspectives for NGS Analysis of DNA and RNA Seq Data |  (Eng)
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	| K. C., Niranjan V., Vishal A., Setlur A. | 
		| Том 19, № 1 (2024) | Interplay of miRNA-TF-Gene Through a Novel Six-node Feed-forward Loop Identified Inflammatory Genes as Key Regulators in Type-2 Diabetes |  (Eng)
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	| Bhat G., Keshav T., Hariharapura R., Fayaz S. | 
		| Том 19, № 2 (2024) | Investigation of LncRNAs Expression as a Potential Biomarker in the Diagnosis and Treatment of Human Brucellosis |  (Eng)
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	| Khaledi M., Haddadi M., Aziziraftar S., Neamati F., Sahebkar A., Kodori M., Abavisani M., Fathizadeh H. | 
		| Том 19, № 2 (2024) | iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking |  (Eng)
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	| Shujaat M., Yoo S., Tayara H., Chong K. | 
		| Том 19, № 4 (2024) | iPSI(2L)-EDL: a Two-layer Predictor for Identifying Promoters and their Types based on Ensemble Deep Learning |  (Eng)
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	| Xiao X., Hu Z., Luo Z., Xu Z. | 
		| Том 19, № 3 (2024) | Mathematical Modelling and Bioinformatics Analyses of Drug Resistance for Cancer Treatment |  (Eng)
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	| Li L., Zhao T., Hu Y., Ren S., Tian T. | 
		| Том 19, № 7 (2024) | Metabolomics: Recent Advances and Future Prospects Unveiled |  (Eng)
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	| Sharma S., Singh G., Akhter M. | 
		| Том 19, № 1 (2024) | miRNA, siRNA, and lncRNA: Recent Development of Bioinformatics Tools and Databases in Support of Combating Different Diseases |  (Eng)
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	| Chakraborty C., Bhattacharya M., Ranjan Sharma A. | 
		| Том 19, № 9 (2024) | MSSD: An Efficient Method for Constructing Accurate and Stable Phylogenetic Networks by Merging Subtrees of Equal Depth |  (Eng)
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	| Xing J., Song X., Yu M., Wang J., Yu J. | 
		| Том 19, № 4 (2024) | NaProGraph: Network Analyzer for Interactions between Nucleic Acids and Proteins |  (Eng)
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	| Nematzadeh S., Aydin N., Kurt Z., Torkamanian-Afshar M. | 
		| Том 19, № 2 (2024) | Network Propagation-based Identification of Oligometastatic Biomarkers in Metastatic Colorectal Cancer |  (Eng)
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	| Jin Q., Yu K., Zhang X., Huo D., Zhang D., Liu L., Xie H., Liang B., Chen X. | 
		| Том 19, № 8 (2024) | Network Subgraph-based Method: Alignment-free Technique for Molecular Network Analysis |  (Eng)
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	| Zaenudin E., Wijaya E., Mekala V., Ng K. | 
		| Том 19, № 8 (2024) | Optimized Hybrid Deep Learning for Real-Time Pandemic Data Forecasting: Long and Short-Term Perspectives |  (Eng)
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	| Dash S., Giri S., Pani S., Mallik S., Wang M., Qin H. | 
		| Том 19, № 9 (2024) | P4PC: A Portal for Bioinformatics Resources of piRNAs and circRNAs |  (Eng)
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	| Liu Y., Li R., Ding Y., Hei X., Wu F. | 
		| Том 19, № 5 (2024) | Predicting the Risk of Breast Cancer Recurrence and Metastasis based on miRNA Expression |  (Eng)
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	| Lv Y., Wang Y., Zhang Y., Chen S., Yao Y. | 
		| Том 19, № 4 (2024) | Prediction of DNA-binding Sites in Transcriptions Factor in Fur-like Proteins Using Machine Learning and Molecular Descriptors |  (Eng)
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	| Muñoz J., Reyes-Suárez J., Besoain F., Arenas-Salinas M. | 
		| Том 19, № 9 (2024) | Prediction of Drug Pathway-based Disease Classes using Multiple Properties of Drugs |  (Eng)
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	| Chen L., Li L. | 
		| Том 19, № 5 (2024) | Prediction of Plant Ubiquitylation Proteins and Sites by Fusing Multiple Features |  (Eng)
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	| Guan M., Qiu W., Wang Q., Xiao X. | 
		| Том 19, № 7 (2024) | Prediction of Super-enhancers Based on Mean-shift Undersampling |  (Eng)
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	| Cheng H., Ding S., Jia C. | 
		| Том 19, № 9 (2024) | Prospects of Identifying Alternative Splicing Events from Single-Cell RNA Sequencing Data |  (Eng)
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	| Wang J., Yuan L. | 
		| Том 19, № 4 (2024) | QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction |  (Eng)
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	| Gao J., Fu Q., Sun J., Wang Y., Xia Y., Lu Y., Wu H., Chen J. | 
		| Том 19, № 6 (2024) | RDR100: A Robust Computational Method for Identification of Krüppel-like Factors |  (Eng)
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	| Malik A., Kamli M., Sabir J., Phan L., Kim C., Manavalan B. | 
		| Том 19, № 3 (2024) | Recent Advances in the Phylogenetic Analysis to Study Rumen Microbiome |  (Eng)
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	| Wassan J., Wang H., Zheng H. | 
		| Том 19, № 1 (2024) | Recommendations for Bioinformatic Tools in lncRNA Research |  (Eng)
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	| Distefano R., Ilieva M., Rennie S., Uchida S. | 
		| Том 19, № 1 (2024) | Representation Learning of Biological Concepts: A Systematic Review |  (Eng)
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	| Yang Y., Zuo X., Das A., Xu H., Zheng W. | 
		| Том 19, № 5 (2024) | Revealing ANXA6 as a Novel Autophagy-related Target for Pre-eclampsia Based on the Machine Learning |  (Eng)
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	| Zhu B., Geng H., Yang F., Wu Y., Cao T., Wang D., Wang Z. | 
		| Том 19, № 5 (2024) | SCV Filter: A Hybrid Deep Learning Model for SARS-CoV-2 Variants Classification |  (Eng)
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	| Wang H., Gao J. | 
		| Том 19, № 10 (2024) | Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism |  (Eng)
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	| Zheng J., Zhou Y. | 
		| Том 19, № 7 (2024) | Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder |  (Eng)
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	| Liang Y., Li S., You X., Guo Y., Tang J. | 
		| Том 19, № 10 (2024) | STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer |  (Eng)
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	| Fan L., Yang X., Wang L., Zhu X. | 
		| Том 19, № 1 (2024) | SVM-Root: Identification of Root-Associated Proteins in Plants by Employing the Support Vector Machine with Sequence-Derived Features |  (Eng)
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	| Kumar Meher P., Hati S., Sahu T., Pradhan U., Gupta A., Rath S. | 
		| Том 19, № 2 (2024) | Thorough Assessment of Machine Learning Techniques for Predicting Protein-Nucleic Acid Binding Hot Spots |  (Eng)
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	| Zou X., Zhang C., Tang M., Deng L. | 
		| Том 19, № 7 (2024) | Toxicity Prediction for Immune Thrombocytopenia Caused by Drugs Based on Logistic Regression with Feature Importance |  (Eng)
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	| Mentari O., Shujaat M., Tayara H., Chong K. | 
		| Том 19, № 5 (2024) | Transformer and Graph Transformer-Based Prediction of Drug-Target Interactions |  (Eng)
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	| Qian M., Lu W., Zhang Y., Liu J., Wu H., Lu Y., Li H., Fu Q., Shen J., Xiao Y. | 
		| Том 19, № 8 (2024) | Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers |  (Eng)
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	| Xie W., Xu J., Zhao C., Li J., Han S., Shao T., Wang L., Feng W. | 
		| Том 19, № 1 (2024) | Translation of Circular RNAs: Functions of Translated Products and Related Bioinformatics Approaches |  (Eng)
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	| Hwang J., Kook T., Paulus S., Park J. | 
		| Том 19, № 2 (2024) | TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet |  (Eng)
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	| Zhang T., Pan L., Yang Q., Yang G., Han N., Qiao S. | 
	
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