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arXiv 2022年

作者： Agterberg, Joshua Zhang, Anru R. Departments of Electrical and Systems Engineering and Statistics and Data Science University of Pennsylvania United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University United States

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the 2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. © 2022, CC BY.

关键词： Tensors

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images 5

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement

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Are dogs not susceptible to retroviral infections?

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Animal Diseases 2024年第3期4卷 217-223页

作者： Jorge Casseb João Henrique Campos Luciano Rodrigo Lopes Laboratory of Investigation in Dermatology and Immunodefciencies Department of DermatologyUniversity of São Paulo Medical SchoolSão PauloSPBrazil Igen-Institute of Immunogenetics São PauloSPBrazil Bioinformatics and Biomedical Data Science Division Health Informatics DepartmentUniversidade Federal de São Paulo–UNIFESP.Rua Botucatu 862Prédio Leal PradotérreoVila ClementinoCEP 04023-062São PauloSPBrazil

Retroviruses have been proven to cause infections and diseases in a series of mammalian hosts but not in ***,this letter discussed the dog susceptibility to retrovirus infection,encompassing arguments to understand why dogs may have not been infected by retroviruses thus *** potential resistance of retrovirus in dogs enables this provocative short communication to discuss this question,looking at some evolutive *** lineage of canids has shown,throughout its evolutionary history,a smaller accumulation of retroviruses in canid genomes,classifed as endogenous *** this context,the genomes of canids seem to ofer obstacles,which have been evolutionarily conserved,in the face of retroviral infection.

关键词： Retrovirus dogs Endogenous retrovirus

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic

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The Innovation 2024年第3期5卷 30-32页

作者： Yantong Cai Wenbo Hu Yao Pei Hao Zhao Guangchuang Yu Dermatology Hospital Southern Medical UniversityGuangzhou 266003China Department of Bioinformatics School of Basic Medical SciencesSouthern Medical UniversityGuangzhou 510515China Tencent AI Lab Shenzhen 518071China State Key Laboratory of Emerging Infectious Diseases School of Public HealthThe University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health(D24H) Hong Kong Science ParkHong Kong SARChina Institute for AI Industry Research Tsinghua UniversityBeijing 100000China

Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual *** method becomes a trendsetter in bringing the metaverse into vivo *** has initially captured the attention of macroscopic biology,as demonstrated by computed tomography and magnetic resonance imaging,which provide a 3D field of view for diagnostic biological ***,it has also opened up new research opportunities in microscopic imaging,such as achieving clearer de novo protein structure *** this method to the field of biology is particularly significant,as it is refining the approach to studying biological ***,many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D *** article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine,highlighting the broad prospects of neural fields in the future biological *** stand at the threshold of an exciting new era,where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

关键词： neural bringing innovative

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Optimizing patient monitoring to prevent clinical deterioration in surgical wards using machine learning

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American Journal of Surgery 2024年 245卷 116138-116138页

作者： Jung, James J. Pou-Prom, Chloé Mamdani, Muhammad Division of Minimally Invasive Surgery Department of Surgery Duke University United States Department of Biostatistics and Bioinformatics Duke University United States Data Science and Advanced Analytics Unity Health Toronto United States

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Genetic prediction of quantitative traits: a machine learner’s guide focused on height

arXiv

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arXiv 2023年

作者： Bourguignon, Lucie Weis, Caroline Jutzeler, Catherine R. Adamer, Michael Biomedical Data Science Lab D-HEST ETH Zurich Switzerland Schulthess Klinik Switzerland Swiss Institute of Bioinformatics Switzerland Machine Learning and Computational Biology Lab D-BSSE ETH Zurich Switzerland

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics. © 2023, CC BY-NC-ND.

关键词： Forecasting

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Docking of Human Band 3 Anion Transporter Proteins with Their Plasmodium falciparum Interactors Based on Short Linear Motifs

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American Journal of Molecular Biology 2024年第4期14卷 187-200页

作者： Fatoumata Gniné Fofana Ayoub Ksouri Cheickna Cisse Oussema Souiai Alia Benkahla Hedmon Okella Mamadou Sangare Jeffrey G. Shaffer Seydou Doumbia Mamadou Wele African Center of Excellence in Bioinformatics University of Sciences Techniques and Technologies of Bamako (USTTB) Bamako Mali Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS) Institut Pasteur de Tunis University of Tunis El Manar Tunis Tunisia School of Veterinary Medicine University of California Davis Tulare USA Department of Biostatistics and Data Science Tulane University School of Public Health and Tropical Medicine New Orleans USA University Clinical Research Centre (UCRC) USTTB Bamako Mali

Plasmodium (P.) falciparum is a pathogen that causes severe forms of malaria. Protein interactions have been shown to occur between P. falciparum and human erythrocytes in human blood. The Band 3 Anion Transporter (B3AT) protein is considered the main invasive pathway for the parasite in erythrocytes that causes clinical symptoms for malaria in humans. The interactions between P. falciparum parasites and erythrocytes along this receptor have previously been explored. Short linear motifs (SLIMs) are short linear mediator sequences that involve several biological processes, acting as mediators of protein interactions identifiable by computational tools such as SLiMFinder. For a given protein, the identification of SLIMs allows predicting its interactors. Using the SLIMs approach, protein-protein interaction network analyses between P. falciparum and its human host, were used to identify a tryptophan-rich protein, A5K5E5_PLAVS as an essential interactor of B3AT. To better understand the interaction mechanism, a guided protein-protein docking approach based on SLIM motifs was performed for human B3AT and A5K5E5_PLAVS. The highlights of this important interaction between P. falciparum and its human host have the potential to pave the way to identify new therapeutic candidates.

关键词： Malaria Protein-Protein Docking Protein-Protein Interaction Short Linear Motifs

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Bringing the Algorithms to the data - Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)

arXiv

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arXiv 2022年

作者： de Arruda Botelho Herr, Marius Graf, Michael Placzek, Peter König, Florian Bötte, Felix Stickel, Tyra Hieber, David Zimmermann, Lukas Slupina, Michael Mohr, Christopher Biergans, Stephanie Akgün, Mete Pfeifer, Nico Kohlbacher, Oliver Institute for Translational Bioinformatics University Hospital Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany Methods in Medical Informatics Department of Computer Science University of Tübingen Germany Applied Bioinformatics Department of Computer Science University of Tübingen Germany Medical Data Integration Center University Hospital Tübingen Tübingen Germany Medical Data Privacy and Privacy-Preserving ML on Healthcare Data Department of Computer Science University of Tübingen Germany

The need for data privacy and security – enforced through increasingly strict data protection regulations – renders the use of healthcare data for machine learning difficult. In particular, the transfer of data between different hospitals is often not permissible and thus cross-site pooling of data not an option. The Personal Health Train (PHT) paradigm proposed within the GO-FAIR initiative implements an’algorithm to the data’ paradigm that ensures that distributed data can be accessed for analysis without transferring any sensitive data. We present PHT-meDIC, a productively deployed open-source implementation of the PHT concept. Containerization allows us to easily deploy even complex data analysis pipelines (e.g, genomics, image analysis) across multiple sites in a secure and scalable manner. We discuss the underlying technological concepts, security models, and governance processes. The implementation has been successfully applied to distributed analyses of large-scale data, including applications of deep neural networks to medical image data. © 2022, CC BY.

关键词： bioinformatics

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