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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Predicting student's psychomotor domain on the vocational senior high school using linear regression 1

Predicting student's psychomotor domain on the vocational se...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Harimurti, R. Yamasari, Y. Ekohariadi Munoto Asto, B.I.G.P. Study Program of Vocational Education on Post Graduate Universitas Negeri Surabaya Surabaya Indonesia Department of Informatics Universitas Negeri Surabaya Surabaya Indonesia

ISBN: (纸本)9781538609545

The educational data can be mined to produce the useful knowledge. This paper focuses on the educational data processing to predict student's psychomotor domain. Here, we apply linear regression method to do it. On process stage, we use 4 regularizations, namely: no regularization, ridge regression, lasso regression and elastic net regression. Furthermore, we exploit 2 sampling methods as the evaluation technique, for examples: cross-validation sampling and random sampling. The experimental result indicates that the best regularization on cross-validation and random sampling are an elastic net regression because this regularization achieves the lowest predicting error. On cross-validation, values of MSE, RMSE, and MAE are 40.079, 6.330 and 5.183, respectively. Additionally, for random sampling, respectively, values of MSE, RMSE, and MAE are 86.910, 8.428 and 6.511. © 2018 IEEE.

关键词： Students

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Retraction Note: A new EEG software that supports emotion recognition by using an autonomous approach

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Neural Computing and Applications 2024年第28期36卷 17759-17759页

作者： Munoz, Roberto Olivares, Rodrigo Taramasco, Carla Villarroel, Rodolfo Soto, Ricardo Alonso-Sánchez, María Francisca Merino, Erick de Albuquerque, Victor Hugo C. Escuela de Ingeniería Civil Informática Universidad de Valparaíso Valparaíso Chile Centro de Investigación y Desarrollo en Ingeniería en Salud Universidad de Valparaíso Valparaíso Chile Pontificia Universidad Católica de Valparaíso Valparaíso Chile Escuela de Fonoaudiología Facultad de Salud Universidad Santo Tomás Viña del Mar Chile Graduate Program in Applied Informatics Laboratory of Bioinformatics University of Fortaleza Fortaleza Brazil

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Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants (vol 11, 955, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 955页

作者： Donovan, Margaret K. R. D'Antonio-Chronowska, Agnieszka D'Antonio, Matteo Frazer, Kelly A. University of California San Diego Bioinformatics and Systems Biology Graduate Program La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Biomedical Informatics La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Department of Pediatrics and Rady Children’s Hospital La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242) University of California San Diego Institute for Genomic Medicine La Jolla USA (GRID:grid.266100.3) (ISNI:0000 0001 2107 4242)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词： Gene expression Genetic association study RNA sequencing

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Framework to Creation of Inclusive and Didactic Digital Material for Elderly

Framework to Creation of Inclusive and Didactic Digital Mate...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Maria Amelia Eliseo Solomon Sunday Oyelere Camilla Almeida da Silva Ismar Frango Silveira Łukasz Tomczyk Mariana Hercovici Cibelle A. de La Higuera Amato Özgür Yaşar Akyar Valéria Farinazzo Martins Computing and Informatics Department Mackenzie Presbyterian University São Paulo Brazil School of Computing University of Eastern Finland Joensuu Finland Institute of Educational Studies Pedagogical University of Cracow Cracow Poland Developmental Disorders Graduate Program Mackenzie Presbyterian University São Paulo Brazil Faculty of Sport Sciences Hacettepe University Ankara Turkey

ISBN: (数字)9789895465903

ISBN: (纸本)9781728167244

The elderly population has been increased due better health conditions and quality of life. With a healthier aging, the elderly start to have a more active and participative life in society. They pursue, among other things, to acquire new knowledge and despite being a generation which access to existing technology was restricted, there is an increase in the use of digital technologies, especially in the educational field. However, with advancing age, cognitive and motor skills decrease. Given this scenario, the creation of accessible didactic materials that include the elderly population is necessary. This paper shows a framework to create inclusive digital material for elderly learning. The framework will help teachers to create digital contents that support elderly learners' personalized educational goals.

关键词： Senior citizens Aging Visualization Statistics informatics Education

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A Step Towards a Reinforcement Learning De Novo Genome Assembler

Research Square

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Research Square 2021年

作者： Padovani, Kleber Xavier, Roberto Carvalho, André Reali, Anna Chateau, Annie Alves, Ronnie Federal University of Pará Computer Science Graduate Program Belém66.075-110 Brazil University of the State of Amazonas Itacoatiara-AM 69.101-416 Brazil Vale Technology Institute Sustainable Development Belém66.055-090 Brazil Institute of Mathematics and Computer Sciences University of São Paulo São Carlos-SP 13566-590 Brazil Polytechnic School University of São Paulo São Paulo-SP 05508-010 Brazil LIRMM-Montpellier Laboratory of Informatics Robotics and Microelectronics Univ Montpellier CNRS Montpellier France

Genome assembly is one of the most relevant and computationally complex tasks in genomics projects. It aims to reconstruct a genome through the analysis of several small textual fragments of such genome — named reads. Ideally, besides ignoring any errors contained in reads, the reconstructed genome should also optimally combine these reads, thus reaching the original genome. The quality of the genome assembly is relevant because the more reliable the genomes, the more accurate the understanding of the characteristics and functions of living beings, and it allows generating many positive impacts on society, including the prevention and treatment of diseases. The assembly becomes even more complex (and it is termed de novo in this case) when the assembler software is not supplied with a similar genome to be used as a reference. Current assemblers have predominantly used heuristic strategies on computational graphs. Despite being widely used in genomics projects, there is still no irrefutably best assembler for any genome, and the proper choice of these assemblers and their configurations depends on Bioinformatics experts. The use of reinforcement learning has proven to be very promising for solving complex activities without human supervision during their learning process. However, their successful applications are predominantly focused on fictional and entertainment problems - such as games. Based on the above, this work aims to shed light on the application of reinforcement learning to solve this relevant real-world problem, the genome assembly. By expanding the only approach found in the literature that addresses this problem, we carefully explored the aspects of intelligent agent learning, performed by the Q-learning algorithm, to understand its suitability to be applied in scenarios whose characteristics are more similar to those faced by real genome projects. The improvements proposed here include changing the previously proposed reward system and including stat

关键词： Reinforcement learning

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AAV-mediated silencing of Sox4 leads to long-term amelioration of liver phenotypes in mouse models of Alagille syndrome

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Gastroenterology 2025年

作者： Duncan Fox Jun Xie Jennifer L Burwinkel Josh M Adams Kashish Chetal Marzieh Keivandarian Yaniv Faingelernt Sanjay Subramanian Mario F Lopez Anna L Peters Nathan Salomonis Neda Zarrin-Khameh Guangping Gao Stacey S Huppert Hamed Jafar-Nejad Department of Molecular and Human Genetics Baylor College of Medicine Houston Texas Genetics and Genomics Graduate Program Baylor College of Medicine Houston Texas. Department of Genetic and Cellular Medicine UMass Chan Medical School Worcester Massachusetts Horae Gene Therapy Center UMass Chan Medical School Worcester Massachusetts Department of Microbiology UMass Chan Medical School Worcester Massachusetts Viral Vector Core UMass Chan Medical School Worcester Massachusetts. Division of Gastroenterology Hepatology and Nutrition Cincinnati Children's Hospital Medical Center Cincinnati Ohio. Program in Developmental Biology Baylor College of Medicine Houston Texas. Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati Ohio Department of Pediatrics University of Cincinnati College of Medicine Cincinnati Ohio. Department of Molecular and Human Genetics Baylor College of Medicine Houston Texas. Division of Gastroenterology Hepatology and Nutrition Cincinnati Children's Hospital Medical Center Cincinnati Ohio Department of Pathology and Immunology Baylor College of Medicine and Ben Taub Hospital Houston Texas. Genetics and Genomics Graduate Program Baylor College of Medicine Houston Texas Program in Developmental Biology Baylor College of Medicine Houston Texas. Electronic address: hamedj@bcm.edu.

BACKGROUND & AIMS:In patients with Alagille syndrome (ALGS), bile duct paucity often leads to severe cholestatic phenotypes for which liver transplantation remains the only definitive treatment. No FDA-approved mechanism-based strategies exist to enhance biliary development in ALGS or other diseases with bile duct paucity. We aimed to identify a therapeutic target to address this unmet need. METHODS:Preclinical ALGS mouse models lacking one copy of Jag1 with or without conditional deletion of one or both copies of Sox9 were used. Sox4 levels were reduced genetically or with adeno-associated virus 8 (AAV8) vectors driving a Sox4-silencing sequence. Liver histology, biliary tree ink injection, serum chemistry, RNAscope (on mouse and human livers), mouse liver single-cell RNA-sequencing (scRNA-Seq), and reanalysis of published human liver bulk RNA-Seq were performed. RESULTS:Conditional removal of one copy of Sox4 in mouse liver significantly improved the ALGS liver phenotypes in a Sox9-dependent manner. An increase in Sox4/SOX4 expression in early postnatal Jag1-heterozygous mouse livers and ALGS patient livers was observed. scRNA-Seq revealed the appearance of an intermediate hepatobiliary cluster co-expressing Sox4 and Sox9 in Jag1-heterozygous livers. AAV8-mediated Sox4 knockdown, ubiquitously or driven by the hepatocyte-specific TBG promoter, led to long-term improvement of ALGS liver phenotypes upon injection at postnatal day 1 (P1). AAV8 injection at P15-after the appearance of liver necrosis-led to the incorporation of some transduced cells into bile ducts and phenotypic improvement. CONCLUSIONS:Preclinical studies provide proof of principle for postnatal AAV-mediated Sox4 knockdown in TBG cells as a therapeutic approach for ALGS liver disease.

关键词： Gene therapy JAG1 Notch signaling cholestasis

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Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

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Journal of Pathology informatics 2023年 14卷 100308-100308页

作者： Fatemi, Michael Feng, Eric Sharma, Cyril Azher, Zarif Goel, Tarushii Ramwala, Ojas Palisoul, Scott M. Barney, Rachael E. Perreard, Laurent Kolling, Fred W. Salas, Lucas A. Christensen, Brock C. Tsongalis, Gregory J. Vaickus, Louis J. Levy, Joshua J. Department of Computer Science University of Virginia Charlottesville VA United States Thomas Jefferson High School for Science and Technology Alexandria VA United States Department of Computer Science Purdue University West Lafayette IN United States Department of Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Biomedical Informatics and Medical Education University of Washington Seattle WA United States Emerging Diagnostic and Investigative Technologies Department of Pathology and Laboratory Medicine Dartmouth Health NH United States Dartmouth Cancer Center NH United States Department of Epidemiology Dartmouth College Geisel School of Medicine Hanover NH United States Department of Molecular and Systems Biology Dartmouth College Geisel School of Medicine Hanover NH United States Integrative Neuroscience at Dartmouth (IND) graduate program Dartmouth College Geisel School of Medicine Hanover NH United States Department of Community and Family Medicine Dartmouth College Geisel School of Medicine Hanover NH United States Department of Dermatology Dartmouth Health NH United States Program in Quantitative Biomedical Sciences Dartmouth College Geisel School of Medicine Hanover NH United States

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural

关键词： Colorectal cancer Deep learning Graph neural network Histomorphology Spatial transcriptomics Transformers

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Metrics for benchmarking and uncertainty quantification: Quality, applicability, and a path to best practices for machine learning in chemistry

arXiv

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arXiv 2020年

作者： Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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Free vibration of orthotropic Levy-type solution plates by using SEM 7

Free vibration of orthotropic Levy-type solution plates by u...

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7th International Conference on Euro Asia Civil Engineering Forum, EACEF 2019

作者： Kiryu, S. Alisjahbana, S.W. Alisjahbana, I. Han, A.L. Gan, B.S. Department of Architecture Graduate School of Engineering Nihon University Koriyama Japan Faculty of Engineering and Informatics Bakrie University Jakarta Indonesia Department of Civil and Environmental Engineering Master Program Student Stanford University CA United States Civil Engineering Department Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Architecture College of Engineering Nihon University Koriyama Japan

Orthotropic Levy-type solution plates considering shear deformation were solved by using the Spectral Element Method (SEM). In SEM, the exact solution of structural dynamic related problems can be solved by using the frequency-dependent shape functions. The solutions of SEM are accurate in decreasing the amount of Degree Of Freedoms (DOF) to discard the cost and computational disadvantages in the Finite Element Method (FEM). This study investigates the free vibration problems of orthotropic Levy-type solution plates solutions based on the two variable Refined Plate Theory (RPT). SEM for orthotropic Levy-type solution plates in the frequency domain was formulated to solve free vibration problems. The differential governing equations of the orthotropic plate element in SEM are formulated into the form of transcendental stiffness matrices. The boundary conditions of the spectral element model consist of four edge-type DOFs at each side of the orthotropic Levy-type solution plates. The natural frequencies of the plate are calculated by means of the Wittrick-Williams procedure. The numerical investigations are presented to show the accuracy, efficiency, and effectiveness of SEM without any discretization scheme. © Published under licence by IOP Publishing Ltd.

关键词： Stiffness matrix

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