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Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

data Intelligence 2022年第2期4卷 342-357页

作者： Stian Soiland-Reyes Genis Bayarri Pau Andrio Robin Long Douglas Lowe Ania Niewielska Adam Hospital Paul Groth Department of Computer Science The University of ManchesterManchester M139PLUK Informatics Institute University of AmsterdamAmsterdam 1098 XHThe Netherlands Institute for Research in Biomedicine(IRB Barcelona) The Barcelona Institute of Science and Technology(BIST)Barcelona O8028Spain The Spanish National Bioinformatics Institute(INB) Barcelona Supercomputing Center(BSC)Barcelona 08034Spain Data Science Institute Lancaster UniversityLancasterLancashire LA14YWUK Research IT IT ServicesThe University of ManchesterManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeshire CB101SDUK

We introduce the concept of Canonical Workflow Building Blocks(CWBB),a methodology of describing and wrapping computational tools,in order for them to be utilised in a reproducible manner from multiple workflow languages and execution *** concept is implemented and demonstrated with the BioExcel Building Blocks library(BioBB),a collection of tool wrappers in the field of computational biomolecular *** across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow,built using this library and run with 5 different Workflow Manager Systems(WfMS).We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research(CWFR)in order to improve widespread adoption and reuse of computational methods across workflow language barriers.

关键词： Scientific workflows Interoperable FAIR Computational tools Containers Software packaging FAIR digital object(FDO),BioExcel Building Blocks library(BioBB),Canonical Workflow Frameworks for Research(CWFR)

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Ontology-based representation and analysis of conditional vaccine immune responses using Omics data 14

Ontology-based representation and analysis of conditional va...

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14th International Conference on Biomedical Ontologies, ICBO 2023

作者： Huffman, Anthony Ong, Edison Brunson, Tim Sanati, Nasim Zheng, Jie Masci, Anna Maria Wu, Guanming He, Yongqun Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States GlaxoSmithKline Rixensart1330 Belgium Oregon Health & Science University PortlandOR United States Department of Genetics University of Pennsylvania Perelman School of Medicine PhiladelphiaPA United States Office of Data Science National Institute of Environmental Health Science DurhamNC United States Unit of Laboratory Animal Medicine University of Michigan Ann ArborMI United States Department of Microbiology and Immunology University of Michigan Ann ArborMI United States Center for Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States

ImmPort, the world’s largest repository of immunology data, includes many vaccine immune response datasets. ImmPort maps the metadata of these studies to ontology and database schema. As of February 28, 2023, our ImmPort data analysis identified 6.258 immune exposures using 47 vaccines in 4,607 human subjects, and 324 cohort studies from the ImmPort. We hypothesized that an integrative ontological representation of the data from these studies would enhance our understanding and analysis of these ImmPort vaccine studies, and with ontological classification and tools such as VIGET, we could further study the effects of different conditions such as vaccine types and host biological sex on the vaccine response gene expression profiles. Our Vaccine Ontology (VO) analysis classified these 37 vaccines into bacterial, viral, and protozoan vaccine types with different vaccine properties. The ImmPort metadata types were modeled with the Vaccine Investigation Ontology (VIO). Our new ontology-based pipeline extracted vaccine response data from the ImmPort database, annotated them based on ontology, obtained corresponding gene expression data from the GEO, and performed consistent omics data analysis. Our use case found gene profiles shared and differed from live and killed inactivated influenza vaccines. Furthermore, our Omics data analysis using the VIGET tool found that female and male human subjects have differential host responses for influenza vaccines. For example, our study showed much stronger early female responses to influenza vaccination than males, and males was able to show active immune responses at a later stage. Interestingly, the female (but not male) human subject group also showed significantly enriched neutrophil degranulation at Day 3 after influenza vaccination;however, males (but not females) displayed significantly enriched neutrophil degranulation at Day 14 after influenza vaccination. These mechanisms have been used to find differences between the gene

关键词： Ontology

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Transfer Learning for Pediatric Glucose Forecasting 4

Transfer Learning for Pediatric Glucose Forecasting

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4th Machine Learning for Health Symposium, ML4H 2024

作者： Ryser, Alain Feng, Chuhao Scheithauer, Tobias Pfister, Marc Burckhardt, Marie-Anne Bachmann, Sara Marx, Alexander Vogt, Julia E. Department of Computer Science ETH Zurich Zürich Switzerland Department of Statistics Research Center Trustworthy Data Science and Security of the University Alliance Ruhr TU Dortmund University Dortmund Germany Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland Pediatric Pharmacology and Pharmacometrics University Children's Hospital Basel Basel Switzerland Department of Clinical Research University of Basel Basel Switzerland Pediatric Endocrinology and Diabetology University Children's Hospital Basel Basel Switzerland SIB Swiss Institute of Bioinformatics Ecublens Switzerland

Effective blood glucose forecasting is crucial for detecting events such as hypo- or hyperglycemia in people with diabetes, yet remains challenging in domains with only small, heterogeneous datasets, such as in the pediatric field. We present GluTFT, a novel transfer learning approach that allows leveraging models pretrained on publicly available adult diabetes datasets for pediatric glucose forecasting. We systematically evaluate multiple transfer learning strategies, including zero-shot prediction and fine-tuning across the entire dataset as well as specific subgroups of participants. Our extensive experiments reveal that GluTFT excels on the pretraining datasets and significantly outperforms baseline methods when fine-tuned. To validate the clinical relevance of our approach, we evaluate Parkes Error Grids, demonstrating the quality of GluTFT's blood glucose forecasts and its potential for enhancing clinical decision-making for pediatric diabetes. © 2024 A. Ryser, C. Feng, T. Scheithauer, M. Pfister, M.-A. Burckhardt, S. Bachmann, A. Marx & J.E. Vogt.

关键词： Zero-shot learning

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Novel loci and potential mechanisms of major depressive disorder,bipolar disorder,and schizophrenia

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science China(Life sciences) 2022年第1期65卷 167-183页

作者： He Wang Zhenghui Yi Tieliu Shi Center for Bioinformatics and Computational Biology Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai 200030China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijing 100083China

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain *** integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for *** discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after *** identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three *** performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling ***,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on ***,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.

关键词： psychiatric disorders multitrait analysis of genome-wide association analysis novel loci Mendelian randomization causal effects

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Meta-Posterior Consistency for the Bayesian Inference of Metastable Systems

arXiv

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arXiv 2024年

作者： Adams, Zachary P. Mukherjee, Sayan Freie Universität Berlin Department of Mathematics and Computer Science Center for Scalable Data Analytics and Artificial Intelligence Universität Liepzig Germany Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures. However, most real-world systems are only metastable, that is, the dynamics appear to be stable on some time scale, but are in fact unstable over longer time scales. Consistency of inference for metastable systems may not be possible, but one can ask about metaconsistency: Do inference procedures converge when observations are taken over a large but finite time interval, but diverge on longer time scales? In this paper we introduce, discuss, and quantify metaconsistency in a Bayesian framework. We discuss how metaconsistency can be exploited to efficiently infer a model for a sub-system of a larger system, where inference on the global behavior may require much more data. We also discuss the relation between metaconsistency and the spectral properties of the model dynamical system in the case of uniformly ergodic *** Codes 62F15, 60J70 © 2024, CC BY.

关键词： Stochastic systems

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An Efficient System to Collect data for AI Training on Multi-Category Object Counting Task 1

An Efficient System to Collect Data for AI Training on Multi...

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1st International Conference on Computer science and Artificial Intelligence, ICCSAI 2021

作者： Haessel, Brian Abdul Rahman, Munif Faisol Andry, Steven Cenggoro, Tjeng Wawan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

This study focused on the problem of collecting data to train AI for a multi-category object counting task, which is like a standard object counting task, where we need to count the number of a particular type of object, for instance, counting how many people in an image. In multi-category object counting, we need to count more than one type of object by keeping track of the number of each type of object. Although in the real object counting case, we often find that AI needs to count objects with multiple categories, the dataset for that particular task is not publicly available. Meanwhile, to have a robust AI for this task, it needs to be trained with a massive amount of data. Therefore, in this study, we developed a system to efficiently facilitate massive data collection for multi-category object counting. This aim was achieved by a careful design of the user experience in the system. The system has been proved to be useful via Technology Acceptance Model (TAM). © 2021 IEEE.

关键词： data acquisition

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The effect of visual advanced driver assistance systems on a following human driver in a mixed-traffic condition

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Procedia Computer science 2023年 216卷 221-229页

作者： Dedy Ariansyah Bens Pardamean Giandomenico Caruso Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers’ ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-traffic condition.

关键词： sustainable transport management platooning car-following visual advanced driver assistance system virtual realtiy

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Adapting Segment Anything Model for Ulcer Segmentation in Inflammatory Bowel Disease 46

Adapting Segment Anything Model for Ulcer Segmentation in In...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Cai, Lingrui Stidham, Ryan W. Wittrup, Emily Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Division of Gastroenterology Department of Internal Medicine Michigan Medicine Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI United States

ISBN: (纸本)9798350371499

Inflammatory Bowel Disease (IBD) is a widespread gastrointestinal disorder characterized by inflammation and ulceration, affecting millions of individuals. Mucosal injury assessment through colonoscopy plays a crucial role in determining disease severity and therapeutic efficacy in IBD. This study draws inspiration from the Segment Anything Model (SAM) framework and tailors it for ulcer segmentation using a Parameter-Efficient Transfer Learning (PETL) approach and incorporating binary cross-entropy loss with boundary loss. Comparative assessments against state-of-the-art UNet-based convolutional neural network models underscore SAM's superior performance, especially when faced with limited training samples. Our ulcer segmentation models demonstrate Dice, IoU, and 95%HD scores of 61.92±0.69, 75.18±0.38, and 10.07±0.05 in the colon dataset, and 55.60±0.82, 77.00±0.67, and 7.74±0.23 in the ileum dataset, respectively. This study highlights the potential of our approach to significantly enhance the efficiency and accuracy of IBD diagnosis and assessment through cutting-edge computer vision techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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TENSOR-ON-TENSOR REGRESSION: RIEMANNIAN OPTIMIZATION, OVER-PARAMETERIZATION, STATISTICAL-COMPUTATIONAL GAP, AND THEIR INTERPLAY

arXiv

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arXiv 2022年

作者： Luo, Yuetian Zhang, Anru R. Data Science Institute University of Chicago United States Department of Biostatistics & Bioinformatics Department of Computer Science Duke University United States

We study the tensor-on-tensor regression, where the goal is to connect tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without prior knowledge of its intrinsic rank. We propose the Riemannian gradient descent (RGD) and Riemannian Gauss-Newton (RGN) methods and cope with the challenge of unknown rank by studying the effect of rank over-parameterization. We provide the first convergence guarantee for the general tensor-on-tensor regression by showing that RGD and RGN respectively converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized settings. Our theory reveals an intriguing phenomenon: Riemannian optimization methods naturally adapt to over-parameterization without modifications to their implementation. We also prove the statistical-computational gap in scalar-on-tensor regression by a direct low-degree polynomial argument. Our theory demonstrates a "blessing of statistical-computational gap" phenomenon: in a wide range of scenarios in tensor-on-tensor regression for tensors of order three or higher, the computationally required sample size matches what is needed by moderate rank over-parameterization when considering computationally feasible estimators, while there are no such benefits in the matrix settings. This shows moderate rank over-parameterization is essentially "cost-free" in terms of sample size in tensor-on-tensor regression of order three or higher. Finally, we conduct simulation studies to show the advantages of our proposed methods and to corroborate our theoretical findings. © 2022, CC BY.

关键词： Tensors

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The largest genome-wide association study for breast cancer in Taiwanese Han population

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Breast Cancer Research and Treatment 2024年第2期203卷 291-306页

作者： Hsu, Yu-Ching Chen, Hung-Lin Cheng, Chi-Fung Chattopadhyay, Amrita Chen, Pei-Shan Lin, Che-Chen Chiang, Hsiu-Yin Liu, Ting-Yuan Huang, Chi-Hao Kuo, Chin-Chi Chuang, Eric Y. Lu, Tzu-Pin Tsai, Fuu-Jen Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center for Translational Genomic Research Department of Medical Research China Medical University Hospital Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Breast Surgery Department of Surgery China Medical University Hospital Taichung Taiwan Division of Nephrology Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan

Purpose: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. Methods: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. Results: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79–7.66) and 3.55 (2.13–6.14), respectively. Conclusion: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Breast cancer Genome-wide association study Polygenic risk score Taiwanese population

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