Computational and structural biotechnology journal最新文献

{"title":"<i>dandelionR</i>: Single-cell immune repertoire trajectory analysis in R.","authors":"Jiawei Yu, Xiaohan Xu, Nicholas Borcherding, Zewen Kelvin Tuong","doi":"10.1016/j.csbj.2025.06.047","DOIUrl":"10.1016/j.csbj.2025.06.047","url":null,"abstract":"<p><p>Integration of single-cell RNA-sequencing (scRNA-seq) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) is extremely powerful in studying lymphocyte development. A python-based package, <i>Dandelion</i>, introduced the VDJ-feature space method, which addresses the challenge of integrating single-cell AIR data with gene expression data and enhances trajectory analysis results. However, no R-based equivalent or similar methods currently exist. To fill this gap, we present <i>dandelionR</i>, an R implementation of <i>Dandelion</i>'s trajectory analysis workflow, bringing the VDJ feature space construction and trajectory analysis using diffusion maps and absorbing Markov chains to R, offering a new option for scRNA-seq and scVDJ-seq analysis to R users.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2890-2897"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A labeled dataset for AI-based cryo-EM map enhancement.","authors":"Nabin Giri, Xiao Chen, Liguo Wang, Jianlin Cheng","doi":"10.1016/j.csbj.2025.06.041","DOIUrl":"10.1016/j.csbj.2025.06.041","url":null,"abstract":"<p><p>Cryogenic electron microscopy (cryo-EM) has transformed structural biology by enabling near atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate model building. While various methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps: regression maps capturing idealized density distributions, binary classification maps distinguishing structural elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label maps compared to experimental maps. This resource bridges the gap between structural biology and artificial intelligence communities, allowing researchers to develop and benchmark innovative methods for enhancing cryo-EM density maps.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2843-2850"},"PeriodicalIF":4.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets.","authors":"Peizheng Mu, Xiangyan Feng, Lanxin Tong, Jie Huang, Chaoqun Zhu, Fei Wang, Wei Quan, Yuanjun Ma, Yucui Dong, Xiao Zhu","doi":"10.1016/j.csbj.2025.06.045","DOIUrl":"10.1016/j.csbj.2025.06.045","url":null,"abstract":"<p><p>Accurate benchmarking of structural variant (SV) detection is essential for advancing the development and application of human whole-genome sequencing (WGS). A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the variation-awareness and strategic implementation of aligners inherently constrain SV detection algorithms that rely on alignment-based approaches. Traditional benchmarking, which primarily focuses on comparing and matching individual variants, makes it difficult to capture the relationships between multiple adjacent variants. We introduced ASVBM, an improved benchmarking framework that introduces the notion of latent positives and leverages a joint analysis and validation strategy based on local variants. This performance improvement arose from the discovery that multiple smaller variants are nearly equivalent to a larger variant. We comprehensively evaluated the performance of six state-of-the-art variant calling pipelines using real WGS datasets. According to multiple matching criteria, ASVBM employs a joint analysis strategy to uncover potential equivalences between the callset and the benchmark set, thereby reducing false mismatches caused by differences in variant representation. ASVBM is available at https://github.com/zhuxiao/asvbm.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2851-2862"},"PeriodicalIF":4.4,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based antibody design targeting recent H5N1 avian influenza strains.","authors":"Nicholas Santolla, Colby T Ford","doi":"10.1016/j.csbj.2025.06.026","DOIUrl":"10.1016/j.csbj.2025.06.026","url":null,"abstract":"<p><p>In 2025 alone, H5N1 avian influenza is responsible for thousands of infections across various animal species, including avian and mammalian livestock such as chickens and cows, and poses a threat to human health due to avian-to-mammalian transmission. There have been 70 human cases of H5N1 influenza in the United States since April 2024 and, as shown in recent studies, our current antibody defenses are waning. Thus, it is imperative to discover new therapeutics in the fight against more recent strains of the virus. In this study, we present the <i>Frankies</i> framework for automated antibody diffusion and assessment. This pipeline was used to automate the generation of 30 novel anti-HA1 Fv antibody fragment sequences, fold them into 3-dimensional structures, and then dock against a recent H5N1 HA1 antigen structure for binding evaluation. Here we show the utility of artificial intelligence in the discovery of novel antibodies against specific H5N1 strains of interest, which bind similarly to known therapeutic and elicited antibodies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2915-2923"},"PeriodicalIF":4.4,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In silico</i> determination of novel SARS-CoV-2 envelope protein ion channel inhibitors.","authors":"Nina Kobe, Lennart Dreisewerd, Matic Pavlin, Polona Kogovšek, Črtomir Podlipnik, Uroš Grošelj, Miha Lukšič","doi":"10.1016/j.csbj.2025.06.036","DOIUrl":"10.1016/j.csbj.2025.06.036","url":null,"abstract":"<p><p>The SARS-CoV-2 envelope protein (2-E^PRO), a viroporin crucial for viral pathogenesis, is a promising target for antiviral drug development as it is highly conserved and functionally important. Although it is a promising therapeutic target for the treatment of COVID-19, it has often been overlooked in previous studies. In this study, a high-throughput virtual screening of nearly one billion compounds was performed, followed by rigorous filtering and re-docking. Eight best-scoring and chemically versatile lead candidates were identified. In molecular dynamics simulations, three of these ligands showed stable protein-ligand complexes occupying the 2-E^PRO channel pore. Among these, ZINC001799167680 (L3) and ZINC001081252239 (L2) exhibited the strongest binding affinity, with key interactions at residues ASN15, THR11 and GLU8 identified by Molecular Mechanics Poisson-Boltzmann Surface Area analysis. All ligands were compared with the known inhibitor rimantadine and showed stronger binding to the protein. These <i>in silico</i> results highlight the potential of focusing on the 2-E^PRO ion channel in the development of novel COVID-19 therapeutics and pave the way for further <i>in vitro</i> and <i>in vivo</i> studies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2823-2831"},"PeriodicalIF":4.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EpiMolBio: A novel user-friendly bioinformatic program for genetic variability analysis.","authors":"Roberto Reinosa, Paloma Troyano-Hernáez, Ana Valadés-Alcaraz, África Holguín","doi":"10.1016/j.csbj.2025.06.034","DOIUrl":"10.1016/j.csbj.2025.06.034","url":null,"abstract":"<p><strong>Purpose: </strong>Genetic sequence analysis has become essential in many fields of medicine, biology, and epidemiology. However, the currently available tools can pose a challenge for users without advanced computational skills.</p><p><strong>Results: </strong>We present EpiMolBio (https://www.epimolbio.com), a free-to-use software designed with an intuitive, user-friendly interface that enables a broad spectrum of users to explore genetic variability. Its diverse toolkit encompasses sequence processing, conservation and variability analysis, consensus sequence generation, and identification of genome mutation or amino acid changes, including specialized tools for HIV and SARS-CoV-2 analysis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2968-2975"},"PeriodicalIF":4.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maternal <i>Clostridium butyricum</i> supplementation during late gestation and lactation enhances gut bacterial communities, milk quality, and reduces piglet diarrhea.","authors":"Morakot Nuntapaitoon, Piraya Chatthanathon, Matanee Palasuk, Alisa Wilantho, Jakavat Ruampatana, Sissades Tongsima, Sarn Settachaimongkon, Naraporn Somboonna","doi":"10.1016/j.csbj.2025.06.040","DOIUrl":"10.1016/j.csbj.2025.06.040","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Experimental objective: &lt;/strong&gt;Diarrhea is a major cause of piglet mortality, often reported associated with maternal gut bacterial communities (microbiota). Maternal supplementation with probiotic &lt;i&gt;Clostridium butyricum&lt;/i&gt; during late gestation showed to reduce piglet diarrhea during the suckling period. This study thereby investigated the effects of probiotic supplementation on sow gut (feces) microbiota and their potential microbial metabolisms.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Sow and litter performances, including milk compositions and incidences of piglet diarrhea, were recorded from farrowing to weaning of control- supplemented vs. probiotic-supplemented sows. Fecal samples from sows classified as before (Cb=17) and after (Ca=17) probiotic supplementation were analyzed using 16S rRNA gene sequencing and 16S rRNA qPCR, following bioinformatic analyses for alpha-beta diversity, quantitative microbiota, LEfSe (Linear discriminant analysis Effect Size) taxon biomarker analysis, potential microbial metabolism profiles, and statistical correlations with microbial species and clinical data performances.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Probiotic-supplemented sows demonstrated the greater average piglets born alive and lower mummified fetuses (&lt;i&gt;P&lt;/i&gt; &gt; 0.05), and the statistical higher protein and casein contents in their colostrum (&lt;i&gt;P&lt;/i&gt; &lt; 0.05). Following microbiota analyses, no significant difference was observed in operational taxonomic units (OTUs), Chao1, and Shannon alpha-diversity indices between Cb and Ca samples. Nevertheless, Ca sows exhibited higher relative abundances of &lt;i&gt;Clostridium&lt;/i&gt;, SMB53, g_&lt;i&gt;Turicibacter&lt;/i&gt;, &lt;i&gt;Treponema&lt;/i&gt;, &lt;i&gt;Bacillus&lt;/i&gt;, &lt;i&gt;Enterococcus&lt;/i&gt; and &lt;i&gt;Lactobacillus&lt;/i&gt;, while the lower abundances of &lt;i&gt;Oscillospira&lt;/i&gt;, &lt;i&gt;Prevotella&lt;/i&gt;, &lt;i&gt;Phascolarctobacterium&lt;/i&gt; and &lt;i&gt;Ruminococcus&lt;/i&gt;, compared with Cb sows. This highlighted that after the probiotic supplementation showed the sow gut microbiota more abundances of potentially beneficial bacteria, including the supplemented probiotic &lt;i&gt;C. butyricum&lt;/i&gt;, g_&lt;i&gt;Bacillus&lt;/i&gt;, g_&lt;i&gt;Enterococcus&lt;/i&gt; and g_&lt;i&gt;Lactobacillus&lt;/i&gt;, for instances. The finding was consistent with the LEfSe (Linear discriminant analysis Effect Size) taxon biomarker analysis for the Ca group. Several microbial related metabolic pathways in sow feces were altered after probiotic supplement, particularly relevant to amino acid and short-chain fatty acid metabolisms (i.e&lt;i&gt;.,&lt;/i&gt; propanoate and butanoate), xenobiotics biodegradation and lipid metabolism. Supportively, the gut microbiota changes of Ca sows might associate with improved sow performance and milk metabolomic profile.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The maternal probiotic &lt;i&gt;C. butyricum&lt;/i&gt; supplementation during late gestation and lactation showed the improved sows' intestine, milk components, and the reduced piglet diarrhea cases. This helps to understand and support the probiotic supplementation ","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2933-2945"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models.","authors":"Tzu-Hsien Yang, Ying-Hsien Huang, Yuan-Han Lee, Jie-Nan Lai, Kuang-Den Chen, Mindy Ming-Huey Guo, Yan Pan, Chun-Yu Chen, Wei-Sheng Wu, Ho-Chang Kuo","doi":"10.1016/j.csbj.2025.06.037","DOIUrl":"10.1016/j.csbj.2025.06.037","url":null,"abstract":"<p><p>Kawasaki disease (KD) is a leading cause of acquired coronary vasculitis in children and remains a critical diagnostic challenge among febrile pediatric patients. To support frontline pediatricians with a more objective diagnostic tool, we developed and implemented KDpredictor, a machine learning-based model for KD risk identification. KDpredictor leverages only the routine blood test features, including complete blood count with differential count, C-reactive protein, and alanine aminotransferase. It also takes the lead in using age-calibrated eosinophil, platelet, and hemoglobin results. Trained using the light gradient boosting machine algorithm on clinical data from 1,927 KD cases and 45,274 febrile controls, KDpredictor achieved strong performance metrics (auROC: 95.7%, auPRC: 72.4%, recall: 0.89) on a reserved test set, outperforming previous models by at least 3% in auROC and 39.3% in auPRC. Additional explainable AI analyses revealed that several top predictive features in KDpredictor are consistent with prior clinical findings. We also evaluated KDpredictor on three independent cohorts collected in East Asia (Taiwan and China) during the COVID-19 period. KDpredictor achieves recall values of 90.9%, 83.7%, and 91.7% on KD samples identified in three independent medical centers, respectively, indicating its applicability across independent clinical settings. In summary, KDpredictor demonstrates robust generalizability in KD risk identification across populations by using only standard blood samples independent of clinical symptoms. KDpredictor is freely available at https://cosbi.ee.ncku.edu.tw/KD_under7/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2832-2842"},"PeriodicalIF":4.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wheels turning: CHO cell modeling moves into a digital biomanufacturing era: Subtitle: CHO Metabolic Modeling.","authors":"Sandeep Ranpura, Vishwanathgouda Maralingannavar, Alexandra-Gabriela Gheorghe, Edward Ma, James Morrissey, Michael J Betenbaugh, Deniz Demirhan","doi":"10.1016/j.csbj.2025.06.035","DOIUrl":"10.1016/j.csbj.2025.06.035","url":null,"abstract":"<p><p>Recent advancements in biologics production using CHO cells have been partly driven by improved understanding of how variations in the cell culture environment influence cellular metabolism, productivity, and the attributes of the final product. In-silico models serve a valuable role in mapping the effects of various process parameters and media changes on cellular response. Advances in technologies such as data-driven analysis, self-learning systems, and digital twins are reinforcing progress toward smart manufacturing, enabling the real-time control of production processes. Furthermore, kinetic, and constraint-based mechanistic modeling, combined with omics approaches, are becoming increasingly incorporated into the bioprocess development and manufacturing innovation ecosystem. In this review, we cover CHO central metabolism as a foundation for mechanistic modeling and extend the discussion to include various mechanistic modeling approaches, highlighting the incorporation of glycosylation and secretory pathways. Multi-omics approaches provide a deeper understanding of intracellular processes and the dynamic interactions between product quality and pathways. In parallel, to achieve the Industry 4.0 vision of digitalization and machine learning techniques are finding wider adoption in biopharmaceutical development. We discuss the potential applications of these techniques for predictions, inference, optimization, and control. The role of big data analytics and artificial intelligence methods in reinforcing progress towards smart manufacturing and enabling real-time control of production processes is discussed. Finally, we summarize the application of machine learning and hybrid models to CHO bioprocesses, aiming to develop and manufacture drugs more efficiently and at a lower cost for patients.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2796-2813"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FlyPhoneDB2: A computational framework for analyzing cell-cell communication in <i>Drosophila</i> scRNA-seq data integrating AlphaFold-multimer predictions.","authors":"Mujeeb Qadiri, Ying Liu, Ah-Ram Kim, Myeonghoon Han, Eric Zhou, Austin Veal, Tzu-Chiao Lu, Hongjie Li, Yanhui Hu, Norbert Perrimon","doi":"10.1016/j.csbj.2025.06.032","DOIUrl":"10.1016/j.csbj.2025.06.032","url":null,"abstract":"<p><p>Cell-cell communication (CCC) plays a critical role in the physiological regulation of organisms and has been implicated in numerous diseases. Previously, we introduced FlyPhoneDB, a tool designed to explore CCC in <i>Drosophila</i> single-cell RNA-sequencing datasets. The core algorithm of FlyPhoneDB infers tissue-specific signaling events between cell types by calculating cell-cell interaction scores based on curated ligand-receptor (L-R) expression across major signaling pathways. However, the utility of FlyPhoneDB was limited by the relatively small number of available L-R pairs. Here, we present FlyPhoneDB2, a major upgrade featuring a significantly expanded knowledgebase that includes a greater number of L-R pairs, incorporating annotations from mammalian species and structural predictions from AlphaFold-Multimer. In addition, the algorithm has been optimized for improved performance and more effective noise filtering. New functionalities have also been introduced, such as the addition of downstream reporter genes to evaluate pathway activity, multi-sample CCC comparison, and enhanced visualizations summarizing communication at a network level. We demonstrate the utility of FlyPhoneDB2 by analyzing whole-body single-nuclei RNA-seq datasets from flies with gut tumors induced by the Yorkie oncogene. We show that FlyPhoneDB2 not only recapitulates established biological insights into the <i>Drosophila</i> Yorkie tumor model, but also identifies novel potential L-R pairs that may play important roles in tumor-induced cachexia. FlyPhoneDB2 is available at https://www.flyrnai.org/tools/fly_phone_v2/.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2814-2822"},"PeriodicalIF":4.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}