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scAI-SNP: a method for inferring ancestry from single-cell data

BMC methods 2025年第1期2卷 10页

作者： Sung Chul Hong Francesc Muyas Isidro Cortés-Ciriano Sahand Hormoz Department of Data Science Dana-Farber Cancer Institute Boston MA 02215 USA. European Molecular Biology Laboratory European Bioinformatics Institute Hinxton Cambridge CB10 1SD UK. Department of Systems Biology Harvard Medical School Boston MA 02115 USA. Broad Institute of MIT and Harvard Cambridge MA 02142 USA.

Background:Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Methods:Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell dataset, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Results:Using diverse single-cell datasets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Discussion:Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity. Supplementary Information:The online version contains supplementary material available at 10.1186/s44330-025-00029-4.

关键词： Ancestry SNP Single-cell transcriptomics The 1000 Genomes Project scAI-SNP

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

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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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Artificial Intelligence-based Decision Support Systems for Precision and Digital Health

arXiv

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arXiv 2024年

作者： Deliu, Nina Chakraborty, Bibhas MEMOTEF Department Sapienza University of Rome Italy MRC - Biostatistics Unit University of Cambridge United Kingdom Centre for Quantitative Medicine and the Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. © 2024, CC BY-NC-SA.

关键词： Electronic health record

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Bayesian multinomial logistic normal models through marginally latent matrix-T processes

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 255-296页

作者： Justin D. Silverman Kimberly Roche Zachary C. Holmes Lawrence A. David Sayan Mukherjee College of Information Science and Technology Department of Statistics and Institute for Computational and Data Science Penn State University University Park PA Program in Computational Biology and Bioinformatics Duke University Durham NC Department of Molecular Genetics and Microbiology Duke University Durham NC Department of Molecular Genetics and Microbiology and Center for Genomic and Computational Biology Duke University Durham NC Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

关键词： Bayesian statistics multivariate analysis count data microbiome gene expression

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Omnibenchmark (alpha) for continuous and open benchmarking in bioinformatics

arXiv

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arXiv 2024年

作者： Mallona, Izaskun Lütge, Almut Carrillo, Ben Incicau, Daniel Gerber, Reto Sonrel, Anthony Soneson, Charlotte Robinson, Mark D. Department of Molecular Life Sciences University of Zurich Zurich8057 Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich8057 Switzerland Swiss Data Science Centre Zurich8092 Switzerland Friedrich Miescher Institute for Biomedical Research Basel4056 Switzerland

Benchmarking in bioinformatics is a process of designing, running and disseminating rigorous performance evaluations of methods (software). Benchmarking systems facilitate the benchmarking process by providing an entrypoint to store, coordinate and execute concrete benchmarks. We describe an alpha version of a new benchmarking system, Omnibenchmark, to facilitate benchmark formalization and execution in solo and community efforts. Omnibenchmark provides a benchmark definition syntax (in a configuration YAML file), a dynamic workflow generation based on Snakemake, S3-compatible storage handling, and reproducible software environments using EasyBuild, lmod, Apptainer or conda. Tutorials and installation instructions are available from https://***. © 2024, CC BY-SA.

关键词： bioinformatics

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Designing an Energy Tracker and Smart Home Solutions Using Design Thinking Methodology

Designing an Energy Tracker and Smart Home Solutions Using D...

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International Conference on ICT For Smart Society (ICISS)

作者： Siti Elda Hiererra Victor Chandra Jane Ivana Jessica Lynn Wibowo Faisal Asadi Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Business Analytics Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center School of Computer Science Bina Nusantara University Jakarta Indonesia

As energy consumption rises, finding ways to reduce energy usage in homes is crucial. This paper presents a case study using design thinking to create a daily energy tracker that helps users reduce energy consumption. We conduct user research, define the problem, brainstorm solutions (mobile app, smart thermostat, smart plug, physical tracker), and test prototypes. A mobile app that tracks energy usage in real-time and offers personalized tips emerges as the most promising solution. We develop and launch the app, partnering with an energy company. This case study showcases the effectiveness of design thinking in creating a user-centered solution for reducing energy consumption.

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Scalable Bayesian inference for the generalized linear mixed model

arXiv

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arXiv 2024年

作者： Berchuck, Samuel I. Medeiros, Felipe Mukherjee, Sayan Agazzi, Andrea Department of Biostatistics & Bioinformatics Statistical Science Duke University NC27710 United States Department of Ophthalmology University of Miami MiamiFL33136 United States Center for Scalable Data Analysis and Artificial Intelligence Universität Leipzig Leipzig04105 Germany Department of Mathematics Università di Pisa PI Pisa56127 Italy

The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings. The focus of this paper is scalable statistical inference for the GLMM, where we define statistical inference as: (i) estimation of population parameters, and (ii) evaluation of scientific hypotheses in the presence of uncertainty. Artificial intelligence (AI) learning algorithms excel at scalable statistical estimation, but rarely include uncertainty quantification. In contrast, Bayesian inference provides full statistical inference, since uncertainty quantification results automatically from the posterior distribution. Unfortunately, traditional Bayesian inference algorithms, including Markov Chain Monte Carlo (MCMC), become computationally intractable in big data settings. In this paper, we introduce a statistical inference algorithm for GLMM that leverages the scalability of modern AI algorithms with guaranteed uncertainty quantification that accompanies Bayesian inference. Our algorithm is an extension of stochastic gradient MCMC with novel contributions that address the treatment of correlated data (i.e., intractable marginal likelihood) and proper posterior variance estimation. Through theoretical and empirical results, we establish our algorithm’s statistical inference properties, and apply the method in a large electronic health records database. Copyright © 2024, The Authors. All rights reserved.

关键词： Big data

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Energy guided diffusion for generating neurally exciting images 23

Energy guided diffusion for generating neurally exciting ima...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Paweł A. Pierzchlewicz Konstantin F. Willeke Arne F. Nix Pavithra Elumalai Kelli Restivo Tori Shinn Cate Nealley Gabrielle Rodriguez Saumil Patel Katrin Franke Andreas S. Tolias Fabian H. Sinz Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany aand Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX and Department of Electrical and Computer Engineering Rice University Houston TX Institute for Bioinformatics and Medical Informatics Tübingen University Tübingen Germany and Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Neuroscience Baylor College of Medicine Houston TX and Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston TX

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method for studying tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a novel readout architecture inspired by the mechanism of visual attention. This new architecture, which we call attention readout, together with a data-driven convolutional core outperforms previous task-driven models in predicting the activity of neurons in macaque area V4. However, as our predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEI's model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across varying model architectures than the state-of-the-art GA, while at the same time reducing computational costs by a factor of 4.7x, facilitating experimentally challenging closed-loop experiments. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting naturalistic images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus, EGG provides a general and flexible framework to study the coding properties of the visual system in the context of natural ima

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Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health

arXiv

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arXiv 2024年

作者： Zhou, Songchi Song, Ge Sun, Haoqi Zhang, Deyun Leng, Yue Westover, M. Brandon Hong, Shenda National Institute of Health Data Science Peking University China Department of Bioinformatics and Biostatistics Shanghai Jiao Tong University China Department of Neurology Beth Israel Deaconess Medical Center Harvard Medical School United States HeartVoice Medical Technology Department of Psychiatry and Behavioral Sciences University of California San Francisco United States

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the duration of arousal and may hinder research on sleep fragmentation and relevant sleep disorders. To address this issue, we propose a deep learning method for automatic and scalable annotation of continuous sleep depth index (SDI) using existing discrete sleep staging labels. Our approach was validated using polysomnography from over 10,000 recordings across four large-scale cohorts. The results showcased a strong correlation between the decrease in sleep depth index and the increase in duration of arousal. Specific case studies indicated that the sleep depth index captured more nuanced sleep structures than conventional sleep staging. Gaussian mixture models based on the digital biomarkers extracted from the sleep depth index identified two subtypes of sleep, where participants in the disturbed sleep group had a higher prevalence of sleep apnea, insomnia, poor subjective sleep quality, hypertension, and cardiovascular disease. The disturbed subtype was associated with a 42% (hazard ratio 1.42, 95% CI 1.24-1.62) increased risk of mortality and a 29% (hazard ratio 1.29, 95% CI 1.00-1.67) increased risk of fatal cardiovascular disease. Our study underscores the utility of the proposed method for continuous sleep depth annotation, which could reveal more detailed information about the sleep structure and yield novel digital biomarkers for routine clinical use in sleep medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Sleep research

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Incorporating the Knowledge Distillation to Improve the EfficientNet Transfer Learning Capability

Incorporating the Knowledge Distillation to Improve the Effi...

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2020 International Conference on data science and Its Applications, ICoDSA 2020

作者： Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728182353

Due to the Deep Learning requirement for a large training dataset, Transfer Learning has become a central method in the field of Computer Vision, which heavily used Deep Learning. Since the adoption of transfer learning in the field, the performance of the models in computer vision is significantly upgraded. The common transfer learning practice in computer vision research is to use state-of-the-art architectures in the ImageNet dataset as the backbone network. The best performing architecture in ImageNet is currently held by EfficientNet, which is a set of architectures that were progressively grown from a baseline architecture. By observing the EfficientNet development approach, it is natural to hypothesize that the smaller EfficientNet architecture is capable to contain the knowledge of the larger architecture. This hypothesis comes from the fact that the larger EfficientNet architecture was grown from the smaller architecture. Therefore, in this study, we experimented if it is beneficial to transfer the knowledge from the larger architecture to the smaller architecture of the EfficientNet. To achieve this goal, we proposed a transfer learning method that uses Knowledge Distillation as the knowledge transfer mechanism from the larger to smaller architecture. We found that the proposed method is able to upgrade the performance of each of the EfficientNet architecture. Several smaller architectures are even able to outperform the larger architecture that was trained using the standard transfer learning method. © 2020 IEEE.

关键词： Distillation

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