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Feature selection using stochastic gates

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arXiv 2018年

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Statistics Department Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology and Bioinformatics Yale University New HavenCT United States Department of Pathology Yale University New HavenCT United States

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the `0 norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an information-theoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Feature extraction

Numerical equivalence between sph and probabilistic mass transfer methods for lagrangian simulation of dispersioni

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arXiv 2018年

作者： Sole-Mari, Guillem Schmidt, Michael J. Pankavich, Stephen D. Benson, David A. Universitat Politècnica de Catalunya Barcelona Spain UPC-CSIC Barcelona Spain Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States

Several Lagrangian methodologies have been proposed in recent years to simulate advection-dispersion of solutes in fluids as a mass exchange between numerical particles carrying the fluid. In this paper, we unify these methodologies, showing that mass transfer particle tracking (MTPT) algorithms can be framed within the context of smoothed particle hydrodynamics (SPH), provided the choice of a Gaussian smoothing kernel whose bandwidth depends on the dispersion and the time discretization. Numerical simulations are performed for a simple dispersion problem, and they are compared to an analytical solution. Based on the results, we advocate for the use of a kernel bandwidth of the size of the characteristic dispersion length ` = √2D∆t, at least given a "dense enough" distribution of particles, for in this case the mass transfer operation is not just an approximation, but in fact the exact solution, of the solute’s displacement Copyright © 2018, The Authors. All rights reserved.

关键词： Mass transfer

Derivative-free superiorization: Principle and algorithm

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arXiv 2019年

作者： Censor, Yair Garduño, Edgar Helou, Elias S. Herman, Gabor T. Department of Mathematics University of Haifa Mt. Carmel Haifa3498838 Israel Departamento de Ciencias de la Computación Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Cd. Universitaria Mexico CityC.P. 04510 Mexico Department of Applied Mathematics and Statistics University of São Paulo São Carlos São Paulo13566-590 Brazil Computer Science Ph.D. Program Graduate Center City University of New York New YorkNY10016 United States

The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to reduce (not necessarily minimize) target function values. This is done by inserting target-function-reducing perturbations into a feasibility-seeking algorithm while retaining its feasibility-seeking ability and without paying a high computational price. A superiorized algorithm that employs component-wise target function reduction steps is presented. This enables derivative-free superiorization (DFS), meaning that superiorization can be applied to target functions that have no calculable partial derivatives or subgradients. The numerical behavior of our derivative-free superiorization algorithm is illustrated on a data set generated by simulating a problem of image reconstruction from projections. The plots of proximity-target curves of our experiments demonstrate the advantage of the proposed derivative-free superiorization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

Beyond trees: Classification with sparse pairwise dependencies

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arXiv 2018年

作者： Tenzer, Yaniv Moscovich, Amit Dorn, Mary Frances Nadler, Boaz Spiegelman, Clifford Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Los Alamos National Laboratory P.O. Box 1663 MS F600 Los AlamosNM87545 United States Department of Statistics Texas A&M University College StationTX77843 United States

Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and bivariate densities that correspond to the tree edges. In practice, however, observed data may not be well approximated by trees. Yet, motivated by the importance of pairwise dependencies for accurate classification, here we propose to approximate the optimal decision boundary by a sparse linear combination of the univariate and bivariate log-transformed densities. Our proposed approach is semi-parametric in nature: we non-parametrically estimate the univariate and bivariate densities, remove pairs of variables that are nearly independent using the Hilbert-Schmidt independence criteria, and finally construct a linear SVM on the retained log-transformed densities. We demonstrate using both synthetic and real data that our resulting classifier, denoted SLB (Sparse Log-Bivariate density), is competitive with popular classification *** Codes 62H30 Copyright © 2018, The Authors. All rights reserved.

关键词： Binary trees

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

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Research Square

Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

Clinical response to azacitidine in MDS is associated with distinct DNA methylation changes in HSPCs

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Nature Communications 2025年第1期16卷 1-20页

作者： Thoms, Julie A. I. Yan, Feng Hampton, Henry R. Davidson, Sarah Joshi, Swapna Saw, Jesslyn Sarowar, Chowdhury H. Lim, Xin Ying Nunez, Andrea C. Kakadia, Purvi M. Bhuyan, Golam Sarower Zou, Xiaoheng Nguyen, Mary Ghodousi, Elaheh S. Koch, Forrest C. Vafaee, Fatemeh Thompson, I. Richard Karimi, Mohammad M. Pickford, Russell Raftery, Mark J. Hough, Sally Buckland, Griselda Bailey, Michelle Ghodke, Yuvaraj Absar, Noorul Vaughan, Lachlin Pasalic, Leonardo Fong, Chun Y. Kenealy, Melita Hiwase, Devendra K. Stoddart, Rohanna I. Mohammed, Soma Lee, Linda Passam, Freda H. Larsen, Stephen R. Spring, Kevin J. Skarratt, Kristen K. Rebeiro, Patricia Presgrave, Peter Stevenson, William S. Ling, Silvia Tiley, Campbell Fuller, Stephen J. Roncolato, Fernando Enjeti, Anoop K. Hoenemann, Dirk Lemech, Charlotte Jolly, Christopher J. Bohlander, Stefan K. Curtis, David J. Wong, Jason W. H. Unnikrishnan, Ashwin Hertzberg, Mark Olivier, Jake Polizzotto, Mark N. Pimanda, John E. School of Biomedical Sciences University of New South Wales Sydney NSW Australia Bioinformatics Division Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia ANU Clinical Hub for Interventional Research (CHOIR) John Curtin School of Medical Research Canberra ACT Australia Kirby Institute University of New South Wales Sydney NSW Australia School of Clinical Medicine University of New South Wales Sydney NSW Australia Australian Centre for Blood Diseases Monash University Melbourne VIC Australia Leukaemia & Blood Cancer Research Unit Department of Molecular Medicine and Pathology Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney NSW Australia UNSW Data Science Hub University of New South Wales Sydney NSW Australia Comprehensive Cancer Centre School of Cancer and Pharmaceutical Sciences King’s College London London United Kingdom Bioanalytical Mass Spectrometry Facility University of New South Wales Sydney NSW Australia Westmead Hospital Sydney NSW Australia ICPMR Department of Haematology Westmead Hospital Sydney NSW Australia Department of Haematology Austin Health Melbourne VIC Australia Cabrini Hospital Melbourne VIC Australia Department of Haematology Royal Adelaide Hospital Adelaide SA Australia University of New South Wales Sydney NSW Australia Royal North Shore Hospital Sydney NSW Australia Haematology Research Group Heart Research Institute Sydney NSW Australia Institute of Haematology Royal Prince Alfred Hospital Sydney NSW Australia Medical Oncology Group Liverpool Clinical School School of Medicine Western Sydney University and Ingham Institute for Applied Medical Research Liverpool NSW Australia South-West Sydney Clinical Campuses UNSW Medicine & Health Sydney NSW Australia Sydney Medical School Nepean Clinical School Faculty of Medicine and Health University of Sydney

Hypomethylating agents are frontline therapies for myelodysplastic neoplasms (MDS), yet clinical responses remain unpredictable. We conducted a phase 2 trial comparing injectable and oral azacitidine (AZA) administered over one or three weeks per four-week cycle, with the primary objective of investigating whether response is linked to in vivo drug incorporation or DNA hypomethylation. Our findings show that injection results in higher drug incorporation, but lower DNA demethylation per cycle, while global DNA methylation levels in mononuclear cells are comparable between responders and non-responders. However, hematopoietic stem and progenitor cells (HSPCs) from responders exhibit distinct baseline and early treatment-induced CpG methylation changes at regulatory regions linked to tissue patterning, cell migration, and myeloid differentiation. By cycle six—when clinical responses typically emerge—further differential hypomethylation in responder HSPCs suggests marrow adaptation as a driver of improved hematopoiesis. These findings indicate that intrinsic baseline and early drug-induced epigenetic differences in HSPCs may underlie the variable clinical response to AZA in MDS. © The Author(s) 2025.

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Author Correction: Genetic drivers and cellular selection of female mosaic X chromosome loss

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Nature 2024年第8043期636卷 E7页

作者： Aoxing Liu Giulio Genovese Yajie Zhao Matti Pirinen Seyedeh M Zekavat Katherine A Kentistou Zhiyu Yang Kai Yu Caitlyn Vlasschaert Xiaoxi Liu Derek W Brown Georgi Hudjashov Bryan R Gorman Joe Dennis Weiyin Zhou Yukihide Momozawa Saiju Pyarajan Valdislav Tuzov Fanny-Dhelia Pajuste Mervi Aavikko Timo P Sipilä Awaisa Ghazal Wen-Yi Huang Neal D Freedman Lei Song Eugene J Gardner Vijay G Sankaran Aarno Palotie Hanna M Ollila Taru Tukiainen Stephen J Chanock Reedik Mägi Pradeep Natarajan Mark J Daly Alexander Bick Steven A McCarroll Chikashi Terao Po-Ru Loh Andrea Ganna John R B Perry Mitchell J Machiela Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland. liuaoxin@***. Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA. liuaoxin@***. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. liuaoxin@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. liuaoxin@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. liuaoxin@***. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. giulio@***. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA. giulio@***. Department of Genetics Harvard Medical School Boston MA USA. giulio@***. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland. Department of Public Health University of Helsinki Helsinki Finland. Department of Mathematics and Statistics University of Helsinki Helsinki Finland. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA. Cardiovascular Research Center Massachusetts General Hospital Boston MA USA. Department of Ophthalmology Massachusetts Eye and Ear Harvard Medical School Boston MA USA. Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA. Department of Medicine Queen's University Kingston Ontario Canada. Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Cancer Prevention Fellowship Program Division of Cancer Prevention National Cancer Institute Rockville MD USA. Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia. Center for Data and Computational Sciences (C-DACS) VA Cooperative Studies Program VA Boston Health

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

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ACS ES&T air

ACS ES&T air 2024年第4期1卷 283-293页

作者： Xuan Liu Jay R Turner Christopher R Oxford Jacob McNeill Brenna Walsh Emmie Le Roy Crystal L Weagle Emily Stone Haihui Zhu Wenyu Liu Zilin Wei Nicole P Hyslop Jason Giacomo Ann M Dillner Abdus Salam Al-Amin Hossen Zubayer Islam Ihab Abboud Clement Akoshile Omar Amador-Muñoz Nguyen Xuan Anh Araya Asfaw Rajasekhar Balasubramanian Rachel Ying-Wen Chang Craig Coburn Sagnik Dey David J Diner Jinlu Dong Tareq Farrah Paterne Gahungu Rebecca M Garland Michel Grutter de la Mora Sina Hasheminassab Juanette John Jhoon Kim Jong Sung Kim Kristy Langerman Pei-Chen Lee Puji Lestari Yang Liu Tesfaye Mamo Mathieu Martins Olga L Mayol-Bracero Mogesh Naidoo Sang Seo Park Yoav Schechner Robyn Schofield Sachchida N Tripathi Eli Windwer Ming-Tsang Wu Qiang Zhang Michael Brauer Yinon Rudich Randall V Martin Department of Energy Environmental & Chemical Engineering Washington University in St. Louis St. Louis Missouri 63130 United States. Air Quality Research Center University of California Davis Davis California 95616 United States. Department of Chemistry University of Dhaka Dhaka 1000 Bangladesh. Air Quality Research Division Environment and Climate Change Canada Toronto Ontario M3H 5T4 Canada. Department of Physics University of Ilorin Ilorin 240003 Nigeria. Instituto de Ciencias de la Atmósfera y Cambio Climático Universidad Nacional Autónoma de México Mexico City 04510 Mexico. Institute of Geophysics Vietnam Academy of Science and Technology Hanoi 11307 Vietnam. Institute of Geophysics and Space Science Addis Ababa University Addis Ababa 1176 Ethiopia. Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore. Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada. Department of Geography and Environment University of Lethbridge Lethbridge Alberta T1K 3M4 Canada. Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi 110016 India. Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 United States. School of Environment Tsinghua University Beijing 100084 People's Republic of China. Research Laboratories Khalifa University Abu Dhabi 127788 United Arab Emirates. Institute of Applied Statistics University of Burundi Bujumbura BP1550 Burundi. Council for Scientific and Industrial Research Pretoria 0001 South Africa. Unit for Environmental Sciences and Management North-West University Potchefstroom 2531 South Africa. Department of Geography Geo-Informatics and Meteorology University of Pretoria Pretoria 0002 South Africa. Department of Atmospheric Sciences Yonsei University Seoul 03722 Republic of Korea. Department of Community Health and Epidemiology Dalhousie University Halifax Nova Scotia B3

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

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Increased activation product of Complement 4 protein in plasma of individuals with schizophrenia

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Brain, Behavior, and Immunity 2021年 98卷 23-23页

作者： A. Kalinowski J. Liliental L. Anker O. Linkovski C. Culbertson J. Hall R. Pattni C. Sabatti D. Noordsy J.F. Hallmayer E. Mellins J.S. Ballon R. O'Hara D.F. Levinson A.E. Urban Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA 94305 USA Sierra Pacific Mental Illness Research Education and Clinical Center (MIRECC) VA Palo Alto Health Care System Palo Alto CA USA Translational Applications Service Center Stanford University School of Medicine Stanford CA 94305 USA Translational Research and Applied Medicine Stanford University School of Medicine Stanford CA 94305 USA Department of Medicine Stanford University School of Medicine Stanford CA 94305 USA Department of Psychology Bar-Illan University Ramat-Gan Israel Department of Neurology Stanford University School of Medicine Stanford CA 94305 USA Department of Genetics Stanford University School of Medicine Stanford CA 94305 USA Department of Biomedical Data Science and Statistics Stanford University School of Medicine Stanford CA 94305 USA Department of Pediatrics Stanford Program in Immunology Stanford University School of Medicine Stanford CA 94305 USA