Progress in Molecular Biology and Translational Science最新文献

AI in multi-omics analysis in AMR. 人工智能在抗生素耐药性多组学分析中的应用。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-02-21 DOI: 10.1016/bs.pmbts.2026.02.001

Neelja Singhal, Manish Kumar

{"title":"AI in multi-omics analysis in AMR.","authors":"Neelja Singhal, Manish Kumar","doi":"10.1016/bs.pmbts.2026.02.001","DOIUrl":"10.1016/bs.pmbts.2026.02.001","url":null,"abstract":"Antimicrobial resistance (AMR) is a global public health threat that has severely jeopardised years of progress in controlling infectious diseases. The last few years have witnessed a dramatic expansion in the high-throughput \"-omics\" technologies, such as genomics, transcriptomics, proteomics, metabolomics, etc. The multi-omics approaches have established that AMR develops due to complex interactions among various biomolecules, including genetic variations, regulatory networks, differential protein expressions, metabolic adaptations, and environmental pressure. But the scale, heterogeneity and high dimensionality of data generated via multi-omics analyses far exceed the capacity of traditional statistical and rule-based analytical methods. Thus, Artificial Intelligence (AI) is needed for extracting meaningful patterns from the complex high-dimensional biological data generated during multi-omics analyses. Artificial intelligence (AI), encompassing machine learning and deep learning techniques, is a robust way to combine different types of -omics data, model non-linear dependencies, and get predictive and mechanistic insights into AMR. This chapter provides a comprehensive and critical overview of the role of AI in multi-omics analysis of AMR, conceptual foundations, methodological advances, representative applications and translational implications. Additionally, significant applications of AI-driven multi-omics AMR research are discussed. Finally, limitations of this strategy and future directions are examined. To fully realise the potential of AI-driven multi-omics strategies for combating AMR, it will be necessary to address their limitations through standardised data generation, robust validation, and interdisciplinary collaboration.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"222 ","pages":"27-51"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148007262","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

Artificial intelligence in microbial metagenomics. 微生物宏基因组学中的人工智能。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-01-23 DOI: 10.1016/bs.pmbts.2026.01.009

Alisha Ansari, Omprakash Shete, Tarini Shankar Ghosh

{"title":"Artificial intelligence in microbial metagenomics.","authors":"Alisha Ansari, Omprakash Shete, Tarini Shankar Ghosh","doi":"10.1016/bs.pmbts.2026.01.009","DOIUrl":"10.1016/bs.pmbts.2026.01.009","url":null,"abstract":"Rapid advancements in genomic sequencing technologies and similar technological advancements in the area of accessing, isolating, extracting and functional probing of microbes residing in diverse environments has resulted in a deluge of microbiome sequencing and microbial genomic sequencing data. Concomitant developments in the area of data science, specifically in the domains of advanced statistics, and artificial intelligence (AI) can facilitate mining this data to answer complex biological questions and developing translational applications in diverse areas, ranging from health-care to industrial microbiology. For most researchers, information on which AI tools address specific biological questions is scattered across disparate sources. In this chapter, we explore the various applications of AI-based methodologies (using case-studies) in answering different biological questions using microbial genomics and metagenomic data. We also discuss different AI and machine-learning (ML) based approaches to integrate metagenomic data with other \"omics\" data. Finally, we highlight both challenges and possibilities with this rapidly progressing field.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"221 ","pages":"255-276"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693404","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

Artificial Intelligence in the prediction of 3D chromatin structure and gene regulation. 人工智能在三维染色质结构预测和基因调控中的应用。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-02-05 DOI: 10.1016/bs.pmbts.2026.01.018

Antoni Pietryga, Dariusz Plewczynski

引用次数: 0

Live biotherapeutics in cancer therapy. 活体生物疗法在癌症治疗中的应用。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-01-23 DOI: 10.1016/bs.pmbts.2026.01.002

Rasmita Mishra, Sreelakshmi S Kumar, T Sayamsmruti Panda, Soumendu Mahapatra, Punit Prasad

{"title":"Live biotherapeutics in cancer therapy.","authors":"Rasmita Mishra, Sreelakshmi S Kumar, T Sayamsmruti Panda, Soumendu Mahapatra, Punit Prasad","doi":"10.1016/bs.pmbts.2026.01.002","DOIUrl":"10.1016/bs.pmbts.2026.01.002","url":null,"abstract":"Cancer poses a global challenge in diagnostics and therapeutics. Treatments like chemotherapy, radiotherapy, surgery, and immunotherapy have significantly decreased the fatality rate, but drug resistance, therapy side effects, and relapse remain as major concerns. Live biotherapeutics are microorganisms that can be developed as therapeutic agents to modulate cancer pathophysiology and aid in disease management. Live biotherapeutic products (LBPs) have the potential to suppress tumour growth, enhance the effectiveness of conventional therapies, and reduce treatment-related side effects. Dysbiosis in the gut and cancer-specific tissues is linked to cancers of the colon, stomach, pancreas, and liver. Live biotherapeutics aim either to re-establish microbial balance or to employ microbes directly as anticancer tools. Both native and engineered LBPs (bacteria and viruses) represent promising interventions that may form part of next-generation cancer treatment strategies. Their clinical application draws on the integration of microbiology, immunology, synthetic biology, and oncology. LBPs can be used to target cancer cells by delivering antitumour payloads such as immune modulators, toxins, exposing cancer antigens, and molecules for targeted killing. LBPs offer advantages such as reduced systemic toxicity, overcoming drug resistance, and synergy with chemo-, radio-, and immunotherapies. Despite challenges in safety, manufacturing, regulation, and personalization, advances in synthetic biology and omics are enabling precision approaches. Future innovations such as bacteriobots, biocontainment systems, and patient-specific microbiome integration highlight their potential as next-generation cancer therapeutics.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"220 ","pages":"361-403"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229517","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

Fecal microbiota transplantation in liver diseases: Therapeutic potential and associated risks. 肝脏疾病的粪便微生物群移植：治疗潜力和相关风险

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-01-30 DOI: 10.1016/bs.pmbts.2026.01.001

Jayeeta Bhowmick, Arka Bagchi

{"title":"Fecal microbiota transplantation in liver diseases: Therapeutic potential and associated risks.","authors":"Jayeeta Bhowmick, Arka Bagchi","doi":"10.1016/bs.pmbts.2026.01.001","DOIUrl":"10.1016/bs.pmbts.2026.01.001","url":null,"abstract":"Fecal microbiota transplantation (FMT) is a biologically coherent strategy to modulate the gut-liver axis by restoring ecosystem structure and function. This chapter synthesizes current evidence and practice of FMT in various liver disease conditions. In cirrhosis with recurrent hepatic encephalopathy (HE), randomized trials demonstrate adjunctive benefits of FMT, reducing recurrence and hospitalizations as well as improving cognition, with route flexibility (lower-GI infusions or oral capsules) and emerging microbiome predictors of response. In severe alcohol-associated hepatitis and ACLF, early single-center trials suggest fewer infections and short-term survival gains, warranting confirmation in multicenter, blinded studies for further outcomes. For MASLD/MASH, FMT consistently shifts intestinal permeability, bile-acid signatures, and hepatic transcriptomics, although it has not reliably improved MRI-PDFF or insulin resistance in unselected cohorts; future success likely requires phenotype enrichment and function-matched donors or defined consortia. Data in chronic hepatitis B remain exploratory, positioning FMT, if at all, as an adjunct to antivirals. Methods are standardized around rigorous donor screening, controlled manufacturing, indication-specific endpoints, and strain-resolved engraftment analytics linking mechanism to outcome. Refractory Clostridium difficile is the only FDA-approved indication of FMT. Use of FMT in hepatology use should remain protocolled and regulated. Priorities include precision donor matching, next-generation consortia, platform trials, and long-term safety registries.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"220 ","pages":"229-246"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229554","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 in multi-omics analysis in viral diseases. AI在病毒性疾病多组学分析中的应用。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-03-31 DOI: 10.1016/bs.pmbts.2026.02.005

Hiteshi Vaidya, Manoj Kumar

{"title":"AI in multi-omics analysis in viral diseases.","authors":"Hiteshi Vaidya, Manoj Kumar","doi":"10.1016/bs.pmbts.2026.02.005","DOIUrl":"10.1016/bs.pmbts.2026.02.005","url":null,"abstract":"Viral diseases are a serious threat to human health worldwide, and are known to cause long-term health complications as well as epidemics and pandemics. Yet it is difficult to understand how they spread, persist, and damage the body, which requires advanced methods. Multi-omics datasets, including genomics, transcriptomics, epigenomics, proteomics and metabolomics provide a complete view of virus-host interactions. However, the integration and interpretation of these high dimensional datasets remain a major challenge. To overcome this, artificial Intelligence (AI), including machine learning (ML) and network-based approaches, has proven to be a powerful tool to analyze, integrate, and interpret multi-omics data. This chapter discusses examples from studies on SARS-CoV-2, HIV, influenza, EBV and others, where AI has been applied to multi-omics research. These studies show how AI-assisted multi-omics approaches are helping in the advancement of viral disease research by providing better understanding of virus induced cellular changes, identifying biomarkers, designing targeted therapies etc. Therefore, integrating multi-omics with AI enables improved diagnosis, treatment, and prevention of viral diseases. We have also discussed challenges faced in combining multi-omics with AI and highlighted future directions that would help in enhancing AI assisted multi-omics research.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"222 ","pages":"229-240"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148007023","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

Recombinant live biotherapeutics: A new frontier in peptide drug biosynthesis and precision delivery. 重组活生物治疗：多肽药物生物合成和精准递送的新前沿。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2025-12-16 DOI: 10.1016/bs.pmbts.2025.12.002

Meenal Chawla, Omkar Bhange, Bhabatosh Das

{"title":"Recombinant live biotherapeutics: A new frontier in peptide drug biosynthesis and precision delivery.","authors":"Meenal Chawla, Omkar Bhange, Bhabatosh Das","doi":"10.1016/bs.pmbts.2025.12.002","DOIUrl":"10.1016/bs.pmbts.2025.12.002","url":null,"abstract":"Peptide-based therapeutics are widely employed in the treatment and management of various medical conditions, including diabetes, obesity, cancer, rare diseases, and microbial infections. This chapter reviews the evolution of technologies used in peptide production including chemical synthesis, recombinant DNA technology, and other advanced methods that have driven significant progress in pharmaceutical development. However, the development of peptide therapeutics is challenged by issues related to high production costs, limited stability, delivery barriers, and potential toxicity. To overcome these issues, recombinant live biotherapeutics (rLBPs), can be utilized as live microbioreactor, where genetically engineered microorganisms are employed to produce and deliver therapeutic peptides directly within the host. Recombinant LBPs enable accurate, sustained, and targeted delivery of therapeutic peptides with enhanced safety, efficacy and cost-effective manufacturing. Furthermore, this chapter highlights the advancements of genetic engineering tools that have enabled the modification and development of rLBPs as microbioreactor, highlighting their emerging role and therapeutic potential in curing disorders.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"220 ","pages":"175-207"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229193","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-driven gene-sets, networks, pathways, and interactions analyses of multi-omics data. 人工智能驱动的基因集、网络、途径和多组学数据的相互作用分析。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-03-25 DOI: 10.1016/bs.pmbts.2026.01.023

Zongliang Yue, Zeru Zhang, Quanghuy Thanh, Thanh Nguyen, Zhandos Sembay, Hao Chen, Robert Welner, Jake Chen

{"title":"AI-driven gene-sets, networks, pathways, and interactions analyses of multi-omics data.","authors":"Zongliang Yue, Zeru Zhang, Quanghuy Thanh, Thanh Nguyen, Zhandos Sembay, Hao Chen, Robert Welner, Jake Chen","doi":"10.1016/bs.pmbts.2026.01.023","DOIUrl":"10.1016/bs.pmbts.2026.01.023","url":null,"abstract":"High-throughput omics technologies continue to expand the scale and dimensionality of biological datasets across research and clinical environments. Yet transforming these large molecular feature sets into mechanistic insight remains a central challenge in translational bioinformatics. Gene-set and pathway-based approaches address this need by providing biologically meaningful organizational units, such as processes, signaling modules, complexes, or phenotypic signatures, through which experimental signals can be interpreted. PAGER 3.0 represents a major advance in the PAGER knowledge ecosystem, offering an expanded and curated collection of Pathways, Annotated gene-lists and Gene signatures (PAGs), ontology-aware knowledge navigation, weighted enrichment analytics, and structured network-based prioritization tools. This chapter introduces the conceptual foundations of PAG-based analysis, details PAGER 3.0 architecture and analytical workflows, and demonstrates utility through a leukemia single-cell transcriptomics case study. Applications in systems biology, precision medicine, machine learning, and drug repurposing are also discussed.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"221 ","pages":"277-313"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693266","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

The Intersection of AI and genomics in health and disease: Advancements and applications. 人工智能和基因组学在健康和疾病中的交叉：进展和应用。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-01-30 DOI: 10.1016/bs.pmbts.2026.01.013

Love Kaushik, A T Vivek, Simran Arora, Fiza Hamid, Kanka Mukherjee, Niyati Bisht, Sakshi Chaudhary, Jagriti Shukla, Sakshi Nawani, Shailesh Kumar

{"title":"The Intersection of AI and genomics in health and disease: Advancements and applications.","authors":"Love Kaushik, A T Vivek, Simran Arora, Fiza Hamid, Kanka Mukherjee, Niyati Bisht, Sakshi Chaudhary, Jagriti Shukla, Sakshi Nawani, Shailesh Kumar","doi":"10.1016/bs.pmbts.2026.01.013","DOIUrl":"10.1016/bs.pmbts.2026.01.013","url":null,"abstract":"AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"221 ","pages":"71-97"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693372","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

Artificial Intelligence in single-cell and spatial transcriptomics data analyses. 单细胞和空间转录组学数据分析中的人工智能。

3区生物学

Progress in Molecular Biology and Translational Science Pub Date : 2026-01-01 Epub Date: 2026-02-17 DOI: 10.1016/bs.pmbts.2026.01.011

Sangeeta Singh, Sonu Kumar, Dinesh Gupta

{"title":"Artificial Intelligence in single-cell and spatial transcriptomics data analyses.","authors":"Sangeeta Singh, Sonu Kumar, Dinesh Gupta","doi":"10.1016/bs.pmbts.2026.01.011","DOIUrl":"10.1016/bs.pmbts.2026.01.011","url":null,"abstract":"Single-cell (SC) and spatial transcriptomics (ST) have transformed molecular biology by allowing high-resolution profiling of gene expression in individual cells and tissues. These approaches reveal cellular diversity, developmental pathways, and disease processes, yet the resulting datasets are large and complex. Artificial intelligence (AI) especially machine learning (ML) and deep learning (DL) now plays a central role in managing this complexity by automating preprocessing, reducing dimensionality, and supporting cell classification and clustering. AI methods also help integrate multi-omics layers, identify spatial patterns, and infer cellular trajectories, strengthening our ability to interpret biological systems. This chapter examines how AI advances the analysis of single-cell and spatial transcriptomics, focusing on methods such as convolutional neural networks (CNN), graph neural networks, (GNN) and variational autoencoders (VAE). It highlights applications in cancer biology, immunology, and neuroscience, including the prediction of cellular behavior and disease mechanisms relevant to personalized medicine. Remaining challenges include scalability, interpretability, and consistent data standards. The chapter concludes with future directions aimed at improving model transparency, enhancing multi-modal integration, and addressing ethical issues in clinical use, offering researchers a concise guide to applying AI for deeper insights into cellular data.","PeriodicalId":49280,"journal":{"name":"Progress in Molecular Biology and Translational Science","volume":"221 ","pages":"99-124"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693367","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