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SSRN 2023年

作者： Pinzón-Arteaga, Carlos A. Wang, Yinjuan Wei, Yulei Li, Leijie Orsi, Ana Elisa Ribeiro Scatolin, Giovanna Liu, Lizhong Sakurai, Masahiro Ye, Jianfeng Yu, Leqian Li, Bo Jiang, Zongliang Wu, Jun Department of Molecular Biology University of Texas Southwestern Medical Center DallasTX United States School of Animal Sciences AgCenter Louisiana State University Baton RougeLA70810 United States SJTU-Yale Joint Center for Biostatistics and Data Science School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China Department of Genetics and Evolutionary Biology Institute of Biosciences University of São Paulo São Paulo Brazil Lyda Hill Department of Bioinformatics University of Texas Southwestern Medical Center DallasTX United States Department of Animal Sciences Genetics Institute University of Florida GainesvilleFL32610 United States Hamon Center for Regenerative Science and Medicine University of Texas Southwestern Medical Center DallasTX75390 United States Cecil H. and Ida Green Center for Reproductive Biology Sciences University of Texas Southwestern Medical Center DallasTX United States

Understanding blastocyst formation and implantation is critical for improving farm animal reproduction but is hampered by a limited supply of embryos. We developed an efficient method to generate bovine blastocyst-like structures (termed blastoids) via the assembly of trophoblast stem cells and expanded potential stem cells. Bovine blastoids resemble blastocysts in morphology, cell composition, single-cell transcriptomes, and represent an accessible in vitro model for studying bovine embryogenesis © 2023, The Authors. All rights reserved.

关键词： Stem cells

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Deep reinforcement learning for large-scale epidemic control

arXiv

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arXiv 2020年

作者： Libin, Pieter Moonens, Arno Verstraeten, Timothy Perez-Sanjines, Fabian Hens, Niel Lemey, Philippe Nowé, Ann Artificial Intelligence Lab Department of Computer Science Vrije Universiteit Brussel Brussels Belgium KU Leuven University of Leuven Department of Microbiology and Immunology Rega Institute for Medical Research Clinical and Epidemiological Virology Leuven Belgium Interuniversity Institute of Biostatistics and Statistical Bioinformatics Data Science Institute Hasselt University Hasselt Belgium

Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epidemics are non-linear and complex processes. For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza. Firstly, we construct a new epidemiological meta-population model, with 379 patches (one for each administrative district in Great Britain), that adequately captures the infection process of pandemic influenza. Our model balances complexity and computational efficiency such that the use of reinforcement learning techniques becomes attainable. Secondly, we set up a ground truth such that we can evaluate the performance of the "Proximal Policy Optimization" algorithm to learn in a single district of this epidemiological model. Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established. This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space. Moreover, through this experiment, we demonstrate that there can be an advantage to consider collaboration between districts when designing prevention strategies. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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USING PERSISTENT HOMOLOGY TOPOLOGICAL FEATURES TO CHARACTERIZE MEDICAL IMAGES: CASE STUDIES ON LUNG AND BRAIN CANCERS

arXiv

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arXiv 2020年

作者： Moon, Chul Li, Qiwei Xiao, Guanghua Department of Statistical Science Southern Methodist University United States Department of Mathematical Sciences University of Texas at Dallas United States Quantitative Biomedical Research Center Department of Population & Data Sciences Department of Bioinformatics University of Texas Southwestern Medical Center United States

Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using consecutive 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression. © 2020, CC BY-NC-SA.

关键词： Topology

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1201P Novel deep learning model and validation of whole slide images in lung cancer diagnosis

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Annals of Oncology 2024年 35卷 S772-S772页

作者： A.A. Ahmed M. Fawi A. Brychcy M. Abouzid M. Witt E. Kaczmarek Bioinformatics and Computational Biology PUMS - Poznan University of Medical Sciences Poznan Poland Data Science Spider Silk Security DMCC Dubai United Arab Emirates Department of Clinical Patomorphology DeparHeliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences Poznań Poland Department of Physical Pharmacy and Pharmacokinetics PUMS - Poznan University of Medical Sciences Poznan Poland Department of Anatomy PUMS - Poznan University of Medical Sciences Poznan Poland

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Effects of Reminders from SanibitTM Electronic Hand Hygiene System and Incentives on Hand Hygiene Compliance - A Prospective, Four Phased, Observational Study

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The Journal of hospital infection 2025年

作者： Emma C Huang Qian Xu Yang Liu Peng Liu Maiying Kong Jiapeng Huang Department of Anesthesiology Duke University Durham NC USA Department of Anesthesiology & Perioperative Medicine School of Medicine University of Louisville Louisville KY USA. Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville Louisville KY USA Division of Biometrics and Data Science Fosun Pharma Beijing People's Republic of China. Microsensor Labs LLC Chicago IL USA. Department of Bioinformatics and Biostatistics School of Public Health and Information Sciences University of Louisville Louisville KY USA. Microsensor Labs LLC Chicago IL USA. Electronic address: jiapeng.huang@louisville.edu.

BACKGROUND:We aim to comprehensively evaluate the implementation of the Sanibitsystem in the ICU using individualized reminders and incentives and study the impact of these interventions on hand hygiene compliance in a 4 phased, prospective trial at a tertiary care hospital in the USA. METHODS:An automated hand hygiene monitoring systems was installed in a 10-bed neurological intensive care unit. Phase 1: No reminder, Cash incentive. Phase 2a: Lights ONLY, Cash Incentive. Phase 2b: Lights + Vibrations, Cash Incentive. Phase 3: No reminder, Point System Incentive. Phase 4: Lights + Vibrations, Point System Incentive. RESULTS:The accuracy rate detected by the Sanibit, compared with direct observations, was 85.1%. Phase 2a with lights only and cash incentives achieved the most hand hygiene opportunities in the middle of the week. Phase 4 with lights and vibrations reminders and point system incentives achieved the best in terms of total, full and partial hand hygiene compliance while phase 2a with lights only and cash incentives had the highest hand hygiene opportunities. Hand hygiene compliance rates of going-out of room events were significantly lower than those of going-in room events for quick in-and-out room while the opposite held true for long in-and-out room events. CONCLUSIONS:Hand hygiene opportunities, compliance, and patient contact times varied significantly among different reminders/incentives, individual healthcare workers, date of the week and time of the day. Lights and vibration reminders and point system incentives achieved the best hand hygiene compliance. CLINICAL TRIAL REGISTRATION:NCT03948672.

关键词： Sanibit automated hand hygiene monitoring compliance hand hygiene infection

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Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Network and Systems Medicine

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Network and Systems Medicine 2020年第1期3卷 36-56页

作者： Stalidzans, Egils Zanin, Massimiliano Tieri, Paolo Castiglione, Filippo Polster, Annikka Scheiner, Stefan Pahle, Jürgen Stres, Blaž List, Markus Baumbach, Jan Lautizi, Manuela Van Steen, Kristel Schmidt, Harald H.H.W. Computational Systems Biology Group University of Latvia Riga Latvia Latvian Biomedical Reasearch and Study Centre Riga Latvia Centro de Tecnología Biomédica Universidad Politécnica de Madrid Pozuelo de Alarcón Spain CNR National Research Council IAC Institute for Applied Computing Rome Italy Oslo Norway Institute for Mechanics of Materials and Structures Vienna University of Technology Vienna Austria BioQuant Heidelberg University Heidelberg Germany Department of Animal Science University of Ljubljana Ljubljana Slovenia Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia Department of Automation Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Big Data in BioMedicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany Computational Systems Medicine Research Group Chair of Experimental Bioinformatics TUM School of Weihenstephan Technical University of Munich Freising Germany BIO-Systems Genetics GIGA-R University of Liège Liège Belgium BIO3 - Systems Medicine Department of Human Genetics KU Leuven Leuven Belgium Department of Pharmacology and Personalised Medicine Faculty of Health Medicine and Life Science Maastricht University Maastricht Netherlands

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body. © Egils Stalidzans et al. 2020 Published b

关键词： Iterative methods

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Author Correction: PLAS-20k: Extended dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

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Scientific data 2024年第1期11卷 730页

作者： Divya B Korlepara C S Vasavi Rakesh Srivastava Pradeep Kumar Pal Saalim H Raza Vishal Kumar Shivam Pandit Aathira G Nair Sanjana Pandey Shubham Sharma Shruti Jeurkar Kavita Thakran Reena Jaglan Shivangi Verma Indhu Ramachandran Prathit Chatterjee Divya Nayar U Deva Priyakumar IHub-Data International Institute of Information Technology Hyderabad 500032 India. Divison of Physics School of Advanced Sciences Vellore Institute of Technology Chennai 600127 India. Department of Artificial Intelligence School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru 560035 India. Centre for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India. Department of Materials Science and Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi 110016 India. divyanayar@mse.iitd.ac.in. IHub-Data International Institute of Information Technology Hyderabad 500032 India. deva@iiit.ac.in. Centre for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India. deva@iiit.ac.in.

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Blood Pressure Value Prediction in Hemodialysis Patients Using the CNN+GRU Network Architecture

Blood Pressure Value Prediction in Hemodialysis Patients Usi...

引用

IEEE International Conference on Orange Technologies (ICOT)

作者： Hsiang-Wei Hu Wun-Long Jheng En-Han Huang Kuan-Yu Chen Chih-Hao Liu Hsin-Yin Chi Wu Chin-Yu Yu-Cheng Chen Jian-An Wang Hsuan-Ming Lin Supratip Ghose Che-Wen Chen Jhing-Fa Wang National Cheng Kung University Tainan Taiwan International Academia of Biomedical Innovation Technology Taipei Taiwan National Defense Medical Center Taipei Taiwan Acusense Biomedical Technology Corp Tainan Taiwan Institute of Biomedical Electronics and Bioinformatics Taipei Taiwan Chinese Culture University Taipei Taiwan National Cheng Kung University Institute of Data Science National Cheng-Kung University Tainan Taiwan China Medical University Tainan Taiwan

ISBN: (纸本)9781665478434

During clinical operation of Hemodialysis, Blood pressure (BP) variability, affect significant global risks and offshoot complication linked to adverse mortality. However, on this task, a prediction model has not been studied extensively with the advent of big data models. Earlier machine learning models lacked the exploitation of precise modeling regarding temporal context information affecting stratified BP values in the output. Recently deep learning models introduced hierarchical temporal feature engineering and context information for Spatio-temporal tasks, and thus this study geared to exploit the advantages of deep learning models, specially, in the form of the Convolutional Neural Network (CNN) model, variants of RNN models, and adaptive hybrid networks combining gated recurrent units (GRU) and CNN for the time-series tasks. In addition, a hybrid deep architecture of CNN and LSTM was employed to predict BP value and make it more efficient and effective when applied to health care. The benchmark results on our dataset for the validation of the CNN+GRU hybrid deep architecture model yielded promising contributions on the prediction task with most significant accuracy value of 72.7% corresponding to delta 15 mmHg and 83.7% when 20 mmHg.

关键词： Deep learning Adaptation models Predictive models Logic gates Blood pressure data models Convolutional neural networks

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

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