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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

BMC biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Pre-Training Genomic Language Model with Variants for Better Modeling Functional Genomics

SSRN

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SSRN 2025年

作者： Liu, Tianyu Zhang, Xiangyu Ying, Rex Zhao, Hongyu Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT06511 United States Department of Biostatistics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

Sequence-to-function models can predict gene expression from sequence data and be used to link genetic information with transcriptomics data to understand regulatory processes and their effects on complex phenotypes. The genomic language models are pre-trained with large-scale DNA sequences and can generate robust representations of these sequences by learning the genomic context. However, few studies can estimate the predictability of gene expression levels and bridge these two classes of models together to explore individualized gene expression prediction. In this manuscript, we propose UKBioBERT as a DNA language model pre-trained with genetic variants from UK BioBank. We demonstrate that UKBioBERT generates informative embeddings capable of identifying gene functions, and improving gene expression prediction in cell lines, thereby enhancing our understanding of gene expression predictability. Building upon these embeddings, we combine UKBioBERT with state-of-the-art sequence-to-function architectures, Enformer and Borzoi, to create UKBioFormer and UKBioZoi. These models exhibit better performance in predicting highly predictable gene expression levels and can be generalized across different cohorts. Furthermore, UKBioFormer effectively captures the relationship between genetic variants and expression variations, enabling in-silico mutation analyses. Collectively, our findings underscore the value of integrating genomic language models and sequence-to-function approaches for advancing functional genomics research. © 2025, The Authors. All rights reserved.

关键词： Genome

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Oligodendroglia vulnerability in the human dorsal striatum in Parkinson’s disease

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Acta Neuropathologica 2025年第1期149卷 1-24页

作者： Barba-Reyes, Juan M. Harder, Lisbeth Marco Salas, Sergio Jaisa-aad, Methasit Muñoz-Castro, Clara Garma, Leonardo D. Rafati, Nima Nilsson, Mats Hyman, Bradley T. Serrano-Pozo, Alberto Muñoz-Manchado, Ana B. Unit of Cell Biology Department of Neuroscience Institute for Biomedical Research and Innovation of Cádiz (INiBICA) University of Cádiz Cádiz Spain Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden Science for Life Laboratory Department of Biochemistry and Biophysics Stockholm University Stockholm Sweden Institute of Computational Biology Computational Health Center Helmholtz Munich Germany Department of Neurology Massachusetts General Hospital Boston MA United States Harvard Medical School Boston MA United States National Bioinformatics Infrastructure Sweden Science for Life Laboratory Department of Medical Biochemistry and Microbiology Uppsala University Uppsala Sweden Ciber of Mental Health (CIBERSAM) ISCIII Madrid 28029 Spain

Oligodendroglia are the responsible cells for myelination in the central nervous system and their involvement in Parkinson’s disease (PD) is poorly understood. We performed sn-RNA-seq and image-based spatial transcriptomics of human caudate nucleus and putamen (dorsal striatum) from PD and control brain donors to elucidate the diversity of oligodendroglia and how they are affected by the disease. We profiled a total of ~ 200.000 oligodendroglial nuclei, defining 15 subclasses, from precursor to mature cells, 4 of which are disease-associated. These PD-specific populations are characterized by the overexpression of heat shock proteins, as well as distinct expression signatures related to immune responses, myelination alterations, and disrupted cell signaling pathways. We have also identified impairments in cell communication and oligodendrocyte development, evidenced by changes in neurotransmitter receptors expression and cell adhesion molecules. In addition, we observed significant disruptions in oligodendrocyte development, with aberrant differentiation trajectories and shifts in cell proportions, particularly in the transition from mature oligodendrocytes to disease-associated states. Quantitative immunohistochemical analysis revealed decreased myelin levels in the PD striatum, which correlated with transcriptomic alterations. Furthermore, spatial transcriptomics mapping revealed the distinct localization of disease-associated populations within the striatum, with evidence of impaired myelin integrity. Thus, we uncover oligodendroglia as a critical cell type in PD and a potential new therapeutic target for myelin-based interventions. © The Author(s) 2025.

关键词： Myelin Neurodegeneration Oligodendrocyte Sc-RNA-seq Spatial transcriptomics Striatum

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Deep multiple instance learning on heterogeneous graph for drug–disease association prediction

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computers in biology and Medicine 2025年 184卷 109403-109403页

作者： Gu, Yaowen Zheng, Si Zhang, Bowen Kang, Hongyu Jiang, Rui Li, Jiao Beijing100020 China Department of Chemistry New York University NY10027 United States Institute for Artificial Intelligence Department of Computer Science and Technology BNRist Tsinghua University Beijing100084 China Beijing StoneWise Technology Co Ltd. Beijing100080 China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division at the Beijing National Research Center for Information Science and Technology Center for Synthetic and Systems Biology Department of Automation Tsinghua University Beijing100084 China

Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug–disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for DDA prediction in drug–disease heterogeneous networks. However, these approaches rarely develop end-to-end frameworks for path instance-level representation learning as well as the further feature selection and aggregation. By leveraging the abundant topological information in path instances, more fine-grained and interpretable predictions can be achieved. To this end, we introduce deep multiple instance learning into drug repositioning by proposing a novel method called MilGNet. MilGNet employs a heterogeneous graph neural network (HGNN)-based encoder to learn drug and disease node embeddings. Treating each drug–disease pair as a bag, we designed a special quadruplet meta-path form and implemented a pseudo meta-path generator in MilGNet to obtain multiple meta-path instances based on network topology. Additionally, a bidirectional instance encoder enhances the representation of meta-path instances. Finally, MilGNet utilizes a multi-scale interpretable predictor to aggregate bag embeddings with an attention mechanism, providing predictions at both the bag and instance levels for accurate and explainable predictions. Comprehensive experiments on five benchmarks demonstrate that MilGNet significantly outperforms ten advanced methods. Notably, three case studies on one drug (Methotrexate) and two diseases (Renal Failure and Mismatch Repair Cancer Syndrome) highlight MilGNet's potential for discovering new indications, therapies, and generating rational meta-path instances to investigate possible treatment mechanisms. The source code is available at https://***/gu-yaowen/MilGNet. © 2024 Elsevier Ltd

关键词： Graph embeddings

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Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability 2nd

Evaluating Cyber-Crime Using Machine Learning and AI Approac...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Chauhan, Ritu Mehtar, Khushi Kaur, Harleen Alankar, Bhavya Artificial Intelligence and IOT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

The online world has become an essential part of everyday life in India due to its fast expansion and technical improvements. But as technology has advanced, cyber-crimes have also increased dramatically, posing serious risks to the economy, security, and privacy. This research examines the intricate terrain of cybercrimes targeting minors in India, scrutinizing more than 16 lakh cases that have been documented during the three years preceding this one. The study takes an extensive approach, applying machine learning modelling such as Gradient Boosting, AdaBoost, Random Forest, Gaussian Naive Bayes, Bernoulli Naive Bayes, Linear SVM, Logistic Regression, and Gradient Boosting in conjunction with exploratory assessment of data. The findings show different accuracy rates;Random Forest, Gradient Boosting, Linear SVM, and Gaussian Naive Bayes all show an impressive 88% accuracy. AdaBoost and Logistic Regression reach 75%, but Bernoulli Naive Bayes comes in lower at 62%. The comprehension of computational interconnections is improved with correlation matrices. This thorough analysis provides important insights for academics, policymakers, and stakeholders in the field of child cybersecurity by exposing common cyber threats targeting minors and highlighting the role that algorithms for learning may play in detecting and averting these risks for environmental sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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computational Challenges in Deciphering Genomic Structures of Bacteria

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Journal of computer science & Technology 2010年第1期25卷 53-70页

作者：徐鹰 Computational Systems Biology Laboratory Department of Biochemistry and Molecular Biology and Institute of Bioinformatics The University of Georgia BESC BioEnergy Science Center College of Computer Science and Technology Jilin University

This article addresses how the functionalities of the cellular machinery of a bacterium might have constrained the genomic arrangement of its genes during evolution and how we can study such problems using computational approaches, taking full advantage of the rapidly increasing pool of the sequenced bacterial genomes, potentially leading to a much improved understanding of why a bacterial genome is organized in the way it is. This article discusses a number of challenging computational problems in elucidating the genomic structures at multiple levels and the information that is encoded through these genomic structures, gearing towards the ultimate understanding of the governing rules of bacterial genome organization.

关键词： bioinformatics microbial genomics genome structure comparative genome analysis biological pathways

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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MorPhiC Consortium: towards functional characterization of all human genes

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Nature 2025年第8050期638卷 351-359页

作者： Adli, Mazhar Przybyla, Laralynne Burdett, Tony Burridge, Paul W. Cacheiro, Pilar Chang, Howard Y. Engreitz, Jesse M. Gilbert, Luke A. Greenleaf, William J. Hsu, Li Huangfu, Danwei Hung, Ling-Hong Kundaje, Anshul Li, Shuzhao Parkinson, Helen Qiu, Xiaojie Robson, Paul Schürer, Stephan C. Shojaie, Ali Skarnes, William C. Smedley, Damian Studer, Lorenz Sun, Wei Vidović, Dušica Vierbuchen, Thomas White, Brian S. Yeung, Ka Yee Yue, Feng Zhou, Ting Žurauskienė, Justina Zhong, Aaron Zheng, Shujian Zhao, Hengqiang Zhang, Nan Zhang, Zhehao Zhang, Stephen Zeng, Tony Yu, Bo Yordanova, Galabina N. Yang, Dapeng Xing, De Wang, Xianming Wang, Ping Torre, Denis Tang, Wenzhuo Shivalikanjli, Anu Sengupta, Kaustav Seker, Fidan Ventura, Enrique Sapena Ross, Nicolette C. Rivera-Diaz, Jamilex Rana, Arushi Pongpamorn, Praeploy Poirion, Olivier B. Pillai, Ajay Pilarczyk, Marcin Parker, Devon Ning, Gang Neyisci, Ozlem Nasam, Niharika Mittal, Varun Mitchell, Joshua M. Metzl-Raz, Eyal Meng, Chen Melo-Carrillo, Adrian McDonough, Justin A. Marengo, Gabriel Mao, Yuzhen Leite, Davi Lyra Luo, Renhe Liu, Nianping Liu, Dingyu Liu, Zukai Liu, Si Li, Zhaoheng Kraft, Katerina Koci, Orges A. Kirsammer, Gina Kendra, Angelina Hutter, Carolyn Huebner, Aaron J. Huang, Yung-Hsin Hu, Nan Gupta, Dipayan Gupta, Revant Guo, Tingfeng Griffiths, Alexandra M. Goyal, Juhee Glenn, Rachel A. Garofalo, Jessica L. Fukuda, Bryce Nobutoshi Fu, Yihao Flynn, William F. Flores, Jesse Fletcher, Colin F. Fleming, Patrick J. Felsenfeld, Adam L. Eraslan, Basak Doty, Anthony Dahlstrom, Thomas J. Chung, Caty E. Choudhary, Anshul Cho, Hyein S. Chen, Xintong Chen, Sijie Chan, Chia Baroch, Jacob J. Baker, Dylan S. Bai, Tao Avireddy, Rohan N. V. S. R. K. Amiri, Anahita Diniz, Juliana Alcoforado Abbaszadeh, Neda Robert H. Lurie Comprehensive Cancer Center Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago IL United States Department of Biochemistry and Biophysics University of California San Francisco CA United States Omics Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Department of Pharmacology Center for Pharmacogenomics Northwestern University Feinberg School of Medicine Evanston IL United States William Harvey Research Institute Clinical Pharmacology and Precision Medicine Queen Mary University of London London United Kingdom Department of Dermatology Stanford University Stanford CA United States Department of Genetics Stanford University Stanford CA United States Basic Science and Engineering (BASE) Initiative Stanford University Stanford CA United States Department of Urology University of California San Francisco CA United States Department of Biostatistics Public Health Sciences Fred Hutchinson Cancer Center Seattle WA United States Developmental Biology Program Sloan Kettering Institute New York NY USA School of Engineering and Technology University of Washington Tacoma Tacoma WA United States Departments of Genetics and Computer Science Stanford University Stanford CA United States Jackson Laboratory for Genomic Medicine Farmington CT United States Knowledge Management Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Molecular and Cellular Pharmacology Sylvester Comprehensive Cancer Center University of Miami Coral Gables FL USA Department of Biostatistics University of Washington Seattle WA United States Department of Biochemistry and Molecular Genetics Robert H. Lurie Comprehensive Cancer Center Northwestern University Feinberg School of Medicine Chicago IL United States

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions. © 2025. Springer Nature Limited.

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