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Nature computational science 2023年第9期3卷 805页

作者： Xi Chen Yuan Wang Antonio Cappuccio Wan-Sze Cheng Frederique Ruf Zamojski Venugopalan D Nair Clare M Miller Aliza B Rubenstein German Nudelman Alicja Tadych Chandra L Theesfeld Alexandria Vornholt Mary-Catherine George Felicia Ruffin Michael Dagher Daniel G Chawla Alessandra Soares-Schanoski Rachel R Spurbeck Lishomwa C Ndhlovu Robert Sebra Steven H Kleinstein Andrew G Letizia Irene Ramos Vance G Fowler Christopher W Woods Elena Zaslavsky Olga G Troyanskaya Stuart C Sealfon Center for Computational Biology Flatiron Institute New York NY USA. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. Department of Computer Science Princeton University Princeton NJ USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. Division of Infectious Diseases Department of Medicine Duke University School of Medicine Durham NC USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT USA. Battelle Memorial Institute Columbus OH USA. Division of Infectious Diseases Department of Medicine Weill Cornell Medicine New York NY USA. Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA. Department of Pathology and Department of Immunobiology Yale School of Medicine New Haven CT USA. Naval Medical Research Center Silver Spring MD USA. Precision Immunology Institute Icahn School of Medicine at Mount Sinai New York NY USA. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. elena.zaslavsky@mssm.edu. Center for Computational Biology Flatiron Institute New York NY USA. ogt@genomics.princeton.edu. Lewis-Sigler Institute of Integrative Genomics Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Computer Science Princeton University Princeton NJ USA. ogt@genomics.princeton.edu. Department of Neurology Icahn School of Medicine at Mount Sinai New York NY USA. stuart.sealfon@mssm.edu.

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Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

npj metabolic health and disease

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npj metabolic health and disease 2024年第1期2卷 15页

作者： Annalise Schweickart Richa Batra Bryan J Neth Cameron Martino Liat Shenhav Anru R Zhang Pixu Shi Naama Karu Kevin Huynh Peter J Meikle Leyla Schimmel Amanda Hazel Dilmore Kaj Blennow Henrik Zetterberg Colette Blach Pieter C Dorrestein Rob Knight Suzanne Craft Rima Kaddurah-Daouk Jan Krumsiek Tri-Institutional Program in Computational Biology & Medicine Weill Cornell Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine Institute for Computational Biomedicine Englander Institute for Precision Medicine New York NY USA. Department of Neurology Mayo Clinic Rochester MN USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Microbiology New York University Grossman School of Medicine New York NY USA. Department of Biostatistics and Bioinformatics Duke University Durham NC USA. Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS) Hobart TAS Australia. Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC Australia. Baker Department of Cardiovascular Research Translation and Implementation La Trobe University Bundoora VIC Australia. Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA. Department of Psychiatry and Neurochemistry University of Gothenburg Gothenburg Sweden. Duke Molecular Physiology Institute Duke University Durham NC USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Departments of Pediatrics Computer Science and Engineering Bioengineering University of California San Diego La Jolla CA USA. Department of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston Salem NC USA. Duke Institute of Brain Sciences Duke University Durham NC USA. Department of Medicine Duke University Durham NC USA.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

关键词： Diseases Metabolomics

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Text mining systems biology: Turning the microscope back on the observer

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Current Opinion in Systems Biology 2018年 11卷 117-122页

作者： Kong, Xiangmeng Gerstein, Mark B. Program in Computational Biology & Bioinformatics Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Molecular Biophysics & Biochemistry Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States Department of Computer Science Yale University Bass 432 266 Whitney Avenue New Haven 06520 CT United States

In this review, we describe the relationship between systems biology and text mining. On the one hand, text mining functions as a practical tool for systems biology research, which integrates diverse sub-fields and takes an overall, systems-level view of biological phenomena. In this vein, various analyses have been done to recognize biological entities, construct networks of them (e.g. protein–protein interaction maps) and even find disease-associated genes directly from texts. On the other hand, text mining can also be applied to study systems biology itself, giving a ”distant-reading perspective” on the evolution of the field. For example, by examining changes in the frequencies of terms in systems biology publications, we can analyze trends in research focus and in the popularity of systems approaches in various subdomains (e.g., autism). Given these two uses of text mining for systems biology, we close with suggestions for adapting current publication formats for facilitating text mining and enabling its broader use. © 2018

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

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Nature genetics 2023年第3期55卷 519-520页

作者： Ceres Fernandez-Rozadilla Maria Timofeeva Zhishan Chen Philip Law Minta Thomas Stephanie Schmit Virginia Díez-Obrero Li Hsu Juan Fernandez-Tajes Claire Palles Kitty Sherwood Sarah Briggs Victoria Svinti Kevin Donnelly Susan Farrington James Blackmur Peter Vaughan-Shaw Xiao-Ou Shu Jirong Long Qiuyin Cai Xingyi Guo Yingchang Lu Peter Broderick James Studd Jeroen Huyghe Tabitha Harrison David Conti Christopher Dampier Mathew Devall Fredrick Schumacher Marilena Melas Gad Rennert Mireia Obón-Santacana Vicente Martín-Sánchez Ferran Moratalla-Navarro Jae Hwan Oh Jeongseon Kim Sun Ha Jee Keum Ji Jung Sun-Seog Kweon Min-Ho Shin Aesun Shin Yoon-Ok Ahn Dong-Hyun Kim Isao Oze Wanqing Wen Keitaro Matsuo Koichi Matsuda Chizu Tanikawa Zefang Ren Yu-Tang Gao Wei-Hua Jia John Hopper Mark Jenkins Aung Ko Win Rish Pai Jane Figueiredo Robert Haile Steven Gallinger Michael Woods Polly Newcomb David Duggan Jeremy Cheadle Richard Kaplan Timothy Maughan Rachel Kerr David Kerr Iva Kirac Jan Böhm Lukka-Pekka Mecklin Pekka Jousilahti Paul Knekt Lauri Aaltonen Harri Rissanen Eero Pukkala Johan Eriksson Tatiana Cajuso Ulrika Hänninen Johanna Kondelin Kimmo Palin Tomas Tanskanen Laura Renkonen-Sinisalo Brent Zanke Satu Männistö Demetrius Albanes Stephanie Weinstein Edward Ruiz-Narvaez Julie Palmer Daniel Buchanan Elizabeth Platz Kala Visvanathan Cornelia Ulrich Erin Siegel Stefanie Brezina Andrea Gsur Peter Campbell Jenny Chang-Claude Michael Hoffmeister Hermann Brenner Martha Slattery John Potter Konstantinos Tsilidis Matthias Schulze Marc Gunter Neil Murphy Antoni Castells Sergi Castellví-Bel Leticia Moreira Volker Arndt Anna Shcherbina Mariana Stern Bens Pardamean Timothy Bishop Graham Giles Melissa Southey Gregory Idos Kevin McDonnell Zomoroda Abu-Ful Joel Greenson Katerina Shulman Flavio Lejbkowicz Kenneth Offit Yu-Ru Su Robert Steinfelder Temitope Keku Bethany van Guelpen Thomas Hudson Heather Hampel Rachel Pearlman Sonja Berndt Richard Hayes Marie Elena Martinez Sushma Thomas Douglas Corley Paul Pharoah Susanna Larsson Yun Yen Heinz-Jo Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK. Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain. Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA. Division of Genetics and Epidemiology Institute of Cancer Research London UK. Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA. Genomic Medicine Institute Cleveland Clinic Cleveland OH USA. Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA. Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain. Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain. Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain. Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain. Department of Biostatistics School of Public Health University of Washington Seattle WA USA. Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK. Department of Public Health Richard Doll Building University of Oxford Oxford UK. Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA. Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA. Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA. De

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An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

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BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

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Correction to: Dependability enhancing mechanisms for integrated clinical environments

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The Journal of Supercomputing 2018年第7期74卷 3486-3487页

作者： Zhao, Wenbing Yang, Mary Q. Department of Electrical Engineering and Computer Science Cleveland State University Cleveland USA Department of Information Science George Washington Donaghey College of Engineering and Information Technology Little Rock USA Joint Bioinformatics Program of University of Arkansas at Little Rock University of Arkansas for Medical Sciences Little Rock USA

The Acknowledgements published in the original article contain errors. The correct version is given below.

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Author Correction: Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

Nature cancer

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Nature cancer 2023年 2023 May 19页

作者： Do Young Hyeon Dowoon Nam Youngmin Han Duk Ki Kim Gibeom Kim Daeun Kim Jingi Bae Seunghoon Back Dong-Gi Mun Inamul Hasan Madar Hangyeore Lee Su-Jin Kim Hokeun Kim Sangyeop Hyun Chang Rok Kim Seon Ah Choi Yong Ryoul Kim Juhee Jeong Suwan Jeon Yeon Woong Choo Kyung Bun Lee Wooil Kwon Seunghyuk Choi Taewan Goo Taesung Park Young-Ah Suh Hongbeom Kim Ja-Lok Ku Min-Sik Kim Eunok Paek Daechan Park Keehoon Jung Sung Hee Baek Jin-Young Jang Daehee Hwang Sang-Won Lee School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Chemistry and Center for Proteogenome Research Korea University Seoul Republic of Korea. Department of Surgery and Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Seoul Republic of Korea. Creative Research Initiatives Center for Epigenetic Code and Diseases Seoul National University Seoul Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. Department of Pathology Seoul National University College of Medicine Seoul Korea. Department of Computer Science Hanyang University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Department of Statistics Seoul National University Seoul Republic of Korea. Korean Cell Line Bank Laboratory of Cell Biology Cancer Research Institute Seoul National University College of Medicine Seoul Korea. Department of New Biology Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. dpark@ajou.ac.kr. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. keehoon.jung@snu.ac.kr. Institute of Allergy and Clinical Immunology Seoul National University Medical Research Center Seoul Republic of Korea. keehoon.jung@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of

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Correction: Robustness of cancer microbiome signals over a broad range of methodological variation

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Oncogene 2024年第20期43卷 1579页

作者： Gregory D Sepich-Poore Daniel McDonald Evguenia Kopylova Caitlin Guccione Qiyun Zhu George Austin Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica L Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Tal Korem Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Micronoma San Diego CA USA. Feinberg School of Medicine Northwestern University Chicago IL USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Antwerp Belgium. School of Life Sciences Arizona State University Tempe AZ USA. Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Program for Mathematical Genomics Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Kraków Kraków Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Obstetrics and Gynecology Columbia University Irving Medical Center New York NY USA. Department of Bioengineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu.

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