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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Genetic effects on gene expression across human tissues

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NATURE 2017年第7675期550卷 204-+页

作者： Aguet, Francois Brown, Andrew A. Castel, Stephane E. Davis, Joe R. He, Yuan Jo, Brian Mohammadi, Pejman Park, Yoson Parsana, Princy Segre, Ayellet V. Strober, Benjamin J. Zappala, Zachary Cummings, Beryl B. Gelfand, Ellen T. Hadley, Kane Huang, Katherine H. Lek, Monkol Li, Xiao Nedzel, Jared L. Nguyen, Duyen Y. Noble, Michael S. Sullivan, Timothy J. Tukiainen, Taru MacArthur, Daniel G. Getz, Gad Management, Nih program Addington, Anjene Guan, Ping Koester, Susan Little, A. Roger Lockhart, Nicole C. Moore, Helen M. Rao, Abhi Struewing, Jeffery P. Volpi, Simona Collection, Biospecimen Brigham, Lori E. Hasz, Richard Hunter, Marcus Johns, Christopher Johnson, Mark Kopen, Gene Leinweber, William F. Lonsdale, John T. McDonald, Alisa Mestichelli, Bernadette Myer, Kevin Roe, Bryan Salvatore, Michael Shad, Saboor Thomas, Jeffrey A. Walters, Gary Washington, Michael Wheeler, Joseph Bridge, Jason Foster, Barbara A. Gillard, Bryan M. Karasik, Ellen Kumar, Rachna Miklos, Mark Moser, Michael T. Jewell, Scott D. Montroy, Robert G. Rohrer, Daniel C. Valley, Dana Mash, Deborah C. Davis, David A. Sobin, Leslie Barcus, Mary E. Branton, Philip A. Grp, Eqtl Manuscript Working Abell, Nathan S. Balliu, Brunilda Delaneau, Olivier Fresard, Laure Gamazon, Eric R. Garrido-Martin, Diego Gewirtz, Ariel D. H. Gliner, Genna Gloudemans, Michael J. Han, Buhm He, Amy Z. Hormozdiari, Farhad Li, Xin Liu, Boxiang Kang, Eun Yong McDowell, Ian C. Ongen, Halit Palowitch, John J. Peterson, Christine B. Quon, Gerald Ripke, Stephan Saha, Ashis Shabalin, Andrey A. Shimko, Tyler C. Sul, Jae Hoon Teran, Nicole A. Tsang, Emily K. Zhang, Hailei Zhou, Yi-Hui Bustamante, Carlos D. Cox, Nancy J. Guigo, Roderic Kellis, Manolis McCarthy, Mark I. Conrad, Donald F. Eskin, Eleazar Li, Gen Nobel, Andrew B. Sabatti, Chiara Stranger, Barbara E. Wen, Xiaoquan Wright, Fred A. Ardlie, Kristin G. Dermitzakis, Emmanouil T. Lappalainen, Tuuli Battle, Alexis Brown, Christopher D. Engelhardt, Barbara E. Montgomery, Stephen B. Aguet, Francois Ardlie, Kristin G. Cummings, Beryl B. Harvard Univ Broad Inst Massachusetts Inst Technol Cambridge MA 02142 USA Massachusetts Gen Hosp Analyt & Translat Genet Unit Boston MA 02114 USA Massachusetts Gen Massachusetts Gen Hosp Hosp Canc Ctr & Dept Pathol Boston MA 02114 USA Harvard Med Sch Dept Genet Boston MA 02114 USA Stanford Univ Dept Genet Stanford CA 94305 USA Stanford Univ Dept Pathol Stanford CA 94305 USA Ben Gurion Univ Negev Dept Clin Biochem & Pharmacol Fac Hlth Sci Negev IL-84105 Beer Sheva Israel Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Barcelona Inst Sci & Technol Ctr Genom Regulat CRC Barcelona 08003 Spain Univ Pompeu Fabra UPF Barcelona 08002 Spain Univ Geneva Med Sch Dept Genet Med & Dev CH-1211 Geneva Switzerland Univ Geneva Inst Genet & Genom Geneva iG3 CH-1211 Geneva Switzerland Swiss Inst Bioinformat CH-1211 Geneva Switzerland Univ Pennsylvania Perelman Sch Med Dept Genet Philadelphia PA 19104 USA New York Genome Ctr New York NY 10013 USA Columbia Univ rvlecl Ctr Dept Syst Biol New York NY 10032 USA Univ Chicago Dept Publ Hlth Sci Chicago IL 60637 USA Washington Univ Sch Med McDonnell Genome Inst St Louis MO USA Washington Univ Sch Med Dept Genet St Louis MO 63108 USA Washington Univ Sch Med Dept Pathol Immunol St Louis MO 63108 USA Vanderbilt Univ tvlecl Ctr Dept tvleclicine Divis Genet Med Nashville TN 37232 USA Princeton Univ Dept Comp Sci Ctr Stat & Machine Learning Princeton NJ 08540 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Human Genet Los Angeles CA 90095 USA Univ Porto Inst Investigac Inovac Saucle i3S P-4200135 Oporto Portugal Univ Porto Inst Mol Pathol & Immunol IPATIMUP P-4200625 Oporto Portugal Univ Amsterdam Acad Mecl Ctr Dept Clin Epidemiol Biostatist & Bioinformat NL-1105 AZ Amsterdam Netherlands Univ Amsterdam Acad Med Ctr Dept Psychiat NL-1105 AZ Amsterdam Netherlands Princeton Univ Lewis Sigler Inst Princeton NJ 08540 USA Princeton Univ Dept Operat Res & F

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

关键词： Gene regulation Transcriptomics

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Predicting False Positives of Protein-Protein Interaction Data by Semantic Similarity Measures

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CURRENT BIOINFORMATICS 2013年第3期8卷 339-346页

作者： Montanez, George Cho, Young-Rae Carnegie Melon Univ Machine Learning Dept Pittsburgh PA 15213 USA Baylor Univ Dept Comp Sci Bioinformat Program Waco TX 76798 USA

Recent technical advances in identifying protein-protein interactions (PPIs) have generated the genomic-wide interaction data, collectively collectively referred to as the interactome. These interaction data give an insight into the underlying mechanisms of biological processes. However, the PPI data determined by experimental and computational methods include an extremely large number of false positives which are not confirmed to occur in vivo. Filtering PPI data is thus a critical preprocessing step to improve analysis accuracy. Integrating Gene Ontology (GO) data is proposed in this article to assess reliability of the PPIs. We evaluate the performance of various semantic similarity measures in terms of functional consistency. Protein pairs with high semantic similarity are considered highly likely to share common functions, and therefore, are more likely to interact. We also propose a combined method of semantic similarity to apply to predicting false positive PPIs. The experimental results show that the combined hybrid method has better performance than the individual semantic similarity classifiers. The proposed classifier predicted that 58.6% of the S. cerevisiae PPIs from the BioGRID database are false positives.

关键词： Gene ontology protein-protein interactions semantic similarity

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Visible Light Communication: Opportunities, Challenges and the Path to Market

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IEEE COMMUNICATIONS MAGAZINE 2013年第12期51卷 26-32页

作者： Jovicic, Aleksandar Li, Junyi Richardson, Tom Qualcomm Res Res & Dev Program Wireless Commun Bridgewater NJ USA Qualcomm Res Res & Dev Program Networking Bridgewater NJ USA Qualcomm Res Res & Dev Program Machine Learning Bridgewater NJ USA Qualcomm Res Bridgewater NJ USA

Visible light communication is a potentially disruptive form of wireless communication that can supplement radio frequency communication and also uniquely enable novel mobile wireless device use cases. High data rate downlink communication in homes and offices and high accuracy indoor positioning in retail stores are two of the most compelling use cases of this promising new technology. Large-scale commercialization of visible light communication devices will depend on both the development of robust and efficient engineering solutions, and the execution of incremental commercialization strategies.

关键词： Downlink Fixtures IEEE 802.11 Standards Light emitting diodes Lighting Mobile handsets Receivers

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Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach

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PSYCHOLOGICAL MEDICINE 2014年第3期44卷 519-532页

作者： Rocha-Rego, V. Jogia, J. Marquand, A. F. Mourao-Miranda, J. Simmons, A. Frangou, S. Kings Coll London Dept Neuroimaging Inst Psychiat London WC2R 2LS England Kings Coll London NIHR Biomed Res Ctr Mental Hlth South London & Maudsley NHS Fdn Trust London WC2R 2LS England Kings Coll London Inst Psychiat London WC2R 2LS England UCL Dept Comp Sci Ctr Computat Stat & Machine Learning London WC1E 6BT England Kings Coll London MRC Ctr Neurodegenerat Res Inst Psychiat London WC2R 2LS England Icahn Sch Med Mt Sinai Icahn Med Inst Psychosis Res Program New York NY 10029 USA

Background Bipolar disorder (BD) is one of the leading causes of disability worldwide. Patients are further disadvantaged by delays in accurate diagnosis ranging between 5 and 10 years. We applied Gaussian process classifiers (GPCs) to structural magnetic resonance imaging (sMRI) data to evaluate the feasibility of using pattern recognition techniques for the diagnostic classification of patients with BD. Method GPCs were applied to gray (GM) and white matter (WM) sMRI data derived from two independent samples of patients with BD (cohort 1: n=26;cohort 2: n=14). Within each cohort patients were matched on age, sex and IQ to an equal number of healthy controls. Results The diagnostic accuracy of the GPC for GM was 73% in cohort 1 and 72% in cohort 2;the sensitivity and specificity of the GM classification were respectively 69% and 77% in cohort 1 and 64% and 99% in cohort 2. The diagnostic accuracy of the GPC for WM was 69% in cohort 1 and 78% in cohort 2;the sensitivity and specificity of the WM classification were both 69% in cohort 1 and 71% and 86% respectively in cohort 2. In both samples, GM and WM clusters discriminating between patients and controls were localized within cortical and subcortical structures implicated in BD. Conclusions Our results demonstrate the predictive value of neuroanatomical data in discriminating patients with BD from healthy individuals. The overlap between discriminative networks and regions implicated in the pathophysiology of BD supports the biological plausibility of the classifiers.

关键词： Bipolar disorder diagnosis Gaussian process classifiers imaging pattern recognition

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Self-recalibrating classifiers for intracortical brain-computer interfaces

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JOURNAL OF NEURAL ENGINEERING 2014年第2期11卷 026001-26020页

作者： Bishop, William Chestek, Cynthia C. Gilja, Vikash Nuyujukian, Paul Foster, Justin D. Ryu, Stephen I. Shenoy, Krishna V. Yu, Byron M. Carnegie Mellon Univ Dept Machine Learning Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Stanford Inst Neuroinnovat & Translat Neurosci Stanford CA 94305 USA Univ Michigan Dept Biomed Engn Ann Arbor MI 48109 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Sch Med Stanford CA 94305 USA Palo Alto Med Fdn Palo Alto CA 94301 USA Stanford Univ Dept Neurobiol Stanford CA 94305 USA Stanford Univ Neurosci Program Stanford CA 94305 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA

Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a similar to 15% increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.

关键词： brain-computer interface classification stable decoding

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Infinite Mixed Membership Matrix Factorization

Infinite Mixed Membership Matrix Factorization

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IEEE 13th International Conference on Data Mining (ICDM)

作者： Saluja, Avneesh Pakdaman, Mahdi Piao, Dongzhen Parikh, Ankur P. Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15260 USA

ISBN: (纸本)9780769551098

Rating and recommendation systems have become a popular application area for applying a suite of machine learning techniques. Current approaches rely primarily on probabilistic interpretations and extensions of matrix factorization, which factorizes a user-item ratings matrix into latent user and item vectors. Most of these methods fail to model significant variations in item ratings from otherwise similar users, a phenomenon known as the "Napoleon Dynamite" effect. Recent efforts have addressed this problem by adding a contextual bias term to the rating, which captures the mood under which a user rates an item or the context in which an item is rated by a user. In this work, we extend this model in a nonparametric sense by learning the optimal number of moods or contexts from the data, and derive Gibbs sampling inference procedures for our model. We evaluate our approach on the Movie Lens 1M dataset, and show significant improvements over the optimal parametric baseline, more than twice the improvements previously encountered for this task. We also extract and evaluate a DBLP dataset, wherein we predict the number of papers co-authored by two authors, and present improvements over the parametric baseline on this alternative domain as well.

关键词： recommender systems graphical models

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Structured association analysis leads to insight into Saccharomyces cerevisiae gene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

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BMC GENOMICS 2013年第1期14卷 1-17页

作者： Curtis, Ross E. Kim, Seyoung Woolford, John L., Jr. Xu, Wenjie Xing, Eric P. Carnegie Mellon Univ Joint Carnegie Mellon Univ Pittsburgh PhD Program Pittsburgh PA 15213 USA Carnegie Mellon Univ Lane Ctr Computat Biol Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

Background: Association analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant. Results: While eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case

关键词： Gene Ontology Candidate Regulator Saccharomyces Genome Database eQTL Analysis Retrotransposon Insertion

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Infinite Mixed Membership Matrix Factorization

Infinite Mixed Membership Matrix Factorization

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Avneesh Saluja Mahdi Pakdaman Dongzhen Piao Ankur P. Parikh Electrical & Computer Engineering Dept. Carnegie Mellon University Pittsburgh PA Intelligent Systems Program University of Pittsburgh Pittsburgh PA Machine Learning Dept. Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781479931446

Rating and recommendation systems have become a popular application area for applying a suite of machine learning techniques. Current approaches rely primarily on probabilistic interpretations and extensions of matrix factorization, which factorizes a user-item ratings matrix into latent user and item vectors. Most of these methods fail to model significant variations in item ratings from otherwise similar users, a phenomenon known as the "Napoleon Dynamite'' effect. Recent efforts have addressed this problem by adding a contextual bias term to the rating, which captures the mood under which a user rates an item or the context in which an item is rated by a user. In this work, we extend this model in a nonparametric sense by learning the optimal number of moods or contexts from the data, and derive Gibbs sampling inference procedures for our model. We evaluate our approach on the Movie Lens 1M dataset, and show significant improvements over the optimal parametric baseline, more than twice the improvements previously encountered for this task. We also extract and evaluate a DBLP dataset, wherein we predict the number of papers co-authored by two authors, and present improvements over the parametric baseline on this alternative domain as well.

关键词： Context modeling Equations Mathematical model Gold Probabilistic logic Context Data models

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