检索结果-内蒙古大学图书馆

The GTEx Consortium atlas of genetic regulatory effects across human tissues

SCIENCE 2020年第6509期369卷 1318-1330页

作者： Aguet, Francois Barbeira, Alvaro N. Bonazzola, Rodrigo Brown, Andrew Castel, Stephane E. Jo, Brian Kasela, Silva Kim-Hellmuth, Sarah Liang, Yanyu Parsana, Princy Flynn, Elise Fresard, Laure Gamazon, Eric R. Hamel, Andrew R. He, Yuan Hormozdiari, Farhad Mohammadi, Pejman Munoz-Aguirre, Manuel Ardlie, Kristin G. Battle, Alexis Bonazzola, Rodrigo Brown, Christopher D. Cox, Nancy Dermitzakis, Emmanouil T. Engelhardt, Barbara E. Garrido-Martin, Diego Gay, Nicole R. Getz, Gad Guigo, Roderic Hamel, Andrew R. Handsaker, Robert E. He, Yuan Hoffman, Paul J. Hormozdiari, Farhad Im, Hae Kyung Jo, Brian Kasela, Silva Kashin, Seva Kim-Hellmuth, Sarah Kwong, Alan Lappalainen, Tuuli Li, Xiao Liang, Yanyu MacArthur, Daniel G. Mohammadi, Pejman Montgomery, Stephen B. Munoz-Aguirre, Manuel Rouhana, John M. Hormozdiari, Farhad Im, Hae Kyung Kim-Hellmuth, Sarah Ardlie, Kristin G. Getz, Gad Guigo, Roderic Im, Hae Kyung Lappalainen, Tuuli Montgomery, Stephen B. Im, Hae Kyung Lappalainen, Tuuli Lappalainen, Tuuli Anand, Shankara Gabriel, Stacey Getz, Gad Graubert, Aaron Hadley, Kane Handsaker, Robert E. Huang, Katherine H. Kashin, Seva Li, Xiao MacArthur, Daniel G. Meier, Samuel R. Nedzel, Jared L. Balliu, Brunilda Conrad, Don Cotter, Daniel J. Das, Sayantan de Goede, Olivia M. Eskin, Eleazar Eulalio, Tiffany Y. Ferraro, Nicole M. Garrido-Martin, Diego Gay, Nicole R. Getz, Gad Graubert, Aaron Guigo, Roderic Hadley, Kane Hamel, Andrew R. Handsaker, Robert E. He, Yuan Hoffman, Paul J. Hormozdiari, Farhad Hou, Lei Huang, Katherine H. Im, Hae Kyung Jo, Brian Kasela, Silva Kashin, Seva Kellis, Manolis Kim-Hellmuth, Sarah Kwong, Alan Lappalainen, Tuuli Li, Xiao Li, Xin Liang, Yanyu MacArthur, Daniel G. Mangul, Serghei Meier, Samuel R. Mohammadi, Pejman Montgomery, Stephen B. Munoz-Aguirre, Manuel Nachun, Daniel C. Nedzel, Jared L. Nguyen, Duyen Y. Nobel, Andrew B. Park, YoSon Reverter, Ferran Sabatti, Chiara Saha, Ashis Segre, Ayellet, V Stephens, Matthew Strober, Benjamin J. Teran, Nicole A. Todres, Ellen Vinuela, Ana Wang, Gao Wen, Xiaoquan Broad Inst MIT & Harvard Cambridge MA 02142 USA Univ Chicago Dept Med Sect Genet Med 5841 S Maryland Ave Chicago IL 60637 USA Univ Geneva Dept Genet Med & Dev Med Sch Geneva Switzerland Univ Dundee Populat Hlth & Genom Dundee Scotland New York Genome Ctr New York NY 10013 USA Columbia Univ Dept Syst Biol New York NY 10027 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA Max Planck Inst Psychiat Stat Genet Munich Germany Univ Chicago Dept Publ Hlth Sci Chicago IL 60637 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Stanford Univ Dept Pathol Stanford CA 94305 USA Vanderbilt Univ Data Sci Inst Nashville TN USA Univ Cambridge Clare Hall Cambridge England Univ Cambridge MRC Epidemiol Unit Cambridge England Vanderbilt Univ Med Ctr Dept Med Div Genet Med Nashville TN USA Harvard Med Sch Massachusetts Eye & Ear Ocular Genom Inst Boston MA 02115 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD USA Harvard TH Chan Sch Publ Hlth Dept Epidemiol Boston MA USA Scripps Res Translat Inst La Jolla CA USA Scripps Res Inst Dept Integrat Struct & Computat Biol La Jolla CA USA Barcelona Inst Sci & Technol Ctr Genom Regulat CRG Barcelona Catalonia Spain Univ Politecn Catalunya UPC Dept Stat & Operat Res Barcelona Catalonia Spain Univ Penn Perelman Sch Med Dept Genet Philadelphia PA 19104 USA Univ Penn Dept Syst Pharmacol & Translat Therapeut Perelman Sch Med Philadelphia PA 19104 USA Univ Michigan Dept Biostat Ann Arbor MI 48109 USA Univ Geneva Inst Genet & Genom Geneva iGE3 Geneva Switzerland Swiss Inst Bioinformat Geneva Switzerland Stanford Univ Dept Genet Stanford CA 94305 USA Massachusetts Gen Hosp Ctr Canc Boston MA USA Massachusetts Gen Hosp Dept Pathol Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Univ Pompeu Fabra UPF Barcelona Catalonia Spain Harvard Med Sch Dept Genet Boston MA 02115 USA Broad Inst MIT & Harvard Univ Pr

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EMPRESS. IX. Extremely Metal-Poor Galaxies are Very Gas-Rich Dispersion-Dominated Systems: Will JWST Witness Gaseous Turbulent High-z Primordial Galaxies?

arXiv

引用

arXiv 2022年

作者： Isobe, Yuki Ouchi, Masami Nakajima, Kimihiko Ozaki, Shinobu Bouché, Nicolas F. Wise, John H. Xu, Yi Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Univ Lyon Univ Lyon1 ENS de Lyon CNRS Centre de Recherche Astrophysique de Lyon UMR5574 Saint-Genis-LavalF-69230 France Center for Relativistic Astrophysics School of Physics Georgia Institute of Technology AtlantaGA30332 United States Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan European Southern Observatory Karl-Schwarzschild-Straße 2 Garching85748 Germany Observatoire de Genéve Université de Genéve 51 Ch. des Maillettes Versoix1290 Switzerland 650 North A'ohoku Place HiloHI96720 United States Research Center for Space and Cosmic Evolution Ehime University Bunkyo-cho 2-5 Ehime Matsuyama790-8577 Japan Center for Computational Sciences University of Tsukuba Ten-nodai 1-1-1 Tsukuba Ibaraki305-8577 Japan Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom National Institute of Technology Ichinoseki College Hagisho Ichinoseki021-8511 Japan Astronomical Institute Tohoku University 6-3 Aoba Aramaki Aoba-ku Miyagi Sendai980-8578 Japan Department of Physics and Astronomy University of Notre Dame 225 Nieuwland Science Hall Notre DameIN46556 United States Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Astronomy University of Wisconsin-Madison 475 N. Charter Street MadisonWI53706 United States Theoretical Astrophysics Department of Earth & Space S

We present kinematics of 6 local extremely metal-poor galaxies (EMPGs) with low metallicities (0.016 − 0.098 Z☉) and low stellar masses (104.7 − 107.6M☉). Taking deep medium-high resolution (R ∼ 7500) integral-field spectra with 8.2-m Subaru, we resolve the small inner velocity gradients and dispersions of the EMPGs with Hα emission. Carefully masking out sub-structures originated by inflow and/or outflow, we fit 3-dimensional disk models to the observed Hα flux, velocity, and velocity-dispersion maps. All the EMPGs show rotational velocities (vrot) of 5-23 km s−1 smaller than the velocity dispersions (σ0) of 17-31 km s−1, indicating dispersion-dominated (vrot/σ0 = 0.29−0.80 gas 0.9 − 1.0. Comparing our results with other Hα kinematics studies, we find that vrot/σ0 decreases and fgas increases with decreasing metallicity, decreasing stellar mass, and increasing specific star-formation rate. We also find that simulated high-z (z ∼ 7) forming galaxies have gas fractions and dynamics similar to the observed EMPGs. Our EMPG observations and the simulations suggest that primordial galaxies are gas-rich dispersion-dominated systems, which would be identified by the forthcoming James Webb Space Telescope (JWST) observations at z ∼ 7. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

来源：评论

学校读者我要写书评

暂无评论

Fixing the problems of deep neural networks will require better training data and learning algorithms

引用

BEHAVIORAL AND BRAIN SCIENCES 2023年 46卷 e400-e400页

作者： Bowers, Jeffrey S. Malhotra, Gaurav Dujmovic, Marin Montero, Milton Llera Tsvetkov, Christian Biscione, Valerio Puebla, Guillermo Adolfi, Federico Hummel, John E. Heaton, Rachel F. Evans, Benjamin D. Mitchell, Jeffrey Blything, Ryan Anderson, Barton L. Storrs, Katherine R. Fleming, Roland W. Bever, Thomas G. Chomsky, Noam Fong, Sandiway Piattelli-Palmarini, Massimo Chandran, Keerthi S. Paul, Amrita Mukherjee Paul, Avijit Ghosh, Kuntal de Vries, Jelmer Philip Flachot, Alban Morimoto, Takuma Gegenfurtner, Karl R. DiCarlo, James J. Yamins, Daniel L. K. Ferguson, Michael E. Fedorenko, Evelina Bethge, Matthias Bonnen, Tyler Schrimpf, Martin German, Joseph Scott Jacobs, Robert A. Golan, Tal Taylor, JohnMark Schutt, Heiko Peters, Benjamin Sommers, Rowan P. Seeliger, Katja Doerig, Adrien Linton, Paul Konkle, Talia van Gerven, Marcel Kording, Konrad Richards, Blake Kietzmann, Tim C. Lindsay, Grace W. Kriegeskorte, Nikolaus Gur, Moshe Hermann, Katherine Nayebi, Aran van Steenkiste, Sjoerd Jones, Matt Houghton, Conor Kazanina, Nina Sukumaran, Priyanka Kellman, Philip J. Baker, Nicholas Garrigan, Patrick Phillips, Austin Lu, Hongjing Koculak, Marcin Wierzchon, Michal Li, Aedan Y. Mur, Marieke Lin, Hause Linsley, Drew Serre, Thomas Liu, Jianghao Bartolomeo, Paolo Love, Bradley C. Mok, Robert M. Moldoveanu, Mihnea Op de Beeck, Hans Bracci, Stefania Rothkopf, Constantin Bremmer, Frank Fiehler, Katja Dobs, Katharina Triesch, Jochen Slagter, Heleen A. Spratling, Michael W. Srivastava, Nisheeth Sifar, Anjali Srinivasan, Narayanan Summerfield, Christopher Thompson, Jessica A. F. Tarr, Michael J. Veit, Walter Browning, Heather Wichmann, Felix A. Kornblith, Simon Geirhos, Robert Xu, Yaoda Vaziri-Pashkam, Maryam Yovel, Galit Abudarham, Naphtali Univ Bristol Sch Psychol Sci Bristol Avon England Max Planck Gesell Ernst Strungmann Inst ESI Neurosci Cooperat Frankfurt Germany Univ Illinois Dept Psychol Champaign IL USA Univ Sussex Sch Engn & Informat Dept Informat Brighton E Sussex England Aston Univ Sch Psychol Birmingham W Midlands England Univ Sydney Sch Psychol Sydney NSW Australia Univ Auckland Dept Psychol Auckland New Zealand Justus Liebig Univ Giessen Dept Psychol Giessen Germany Univ Marburg & Giessen Ctr Mind Brain & Behav Giessen Germany Univ Arizona Dept Linguist Tucson AZ USA Indian Stat Inst Ctr Soft Comp Res Kolkata India Indian Stat Inst Machine Intelligence Unit Kolkata India IIIT Allahabad Applied Sci Prayagraj Uttarakhand India Tufts Univ Biomed Engn Medford MA USA Justus Liebig Univ Dept Psychol Giessen Germany York Univ Dept Psychol Toronto ON Canada Univ Oxford Dept Expt Psychol Oxford England MIT Quest Intelligence Dept Brain & Cognit Sci Cambridge MA USA MIT McGovern Inst Brain Res Cambridge MA USA Stanford Univ Wu Tsai Neurosci Inst Stanford CA USA Univ Tubingen Tubingen AI Ctr Tubingen Germany Ecole Polytech Fed Lausanne Lausanne Switzerland Univ Calif San Diego Dept Cognit Sci San Diego CA USA Univ Rochester Dept Brain & Cognit Sci Rochester NY USA Ben Gurion Univ Negev Dept Cognit & Brain Sci Beer Sheva Israel Columbia Univ Zuckerman Mind Brain Behav Inst New York NY USA NYU Ctr Neural Sci New York NY USA Univ Glasgow Sch Psychol & Neurosci Glasgow Lanark Scotland Max Planck Inst Psycholinguist Dept Neurobiol Language Nijmegen Netherlands Max Planck Inst Human Cognit & Brain Sci Leipzig Germany Univ Osnabruck Inst Cognit Sci Osnabruck Germany Columbia Univ Ctr Sci & Soc Presidential Scholars Soc & Neurosci New York NY USA Columbia Univ Italian Acad Adv Studies Amer New York NY USA Harvard Univ Dept Psychol & Ctr Brain Sci Cambridge MA USA Donders Inst Brain Cognit & Behav Nijmegen Netherlands Univ Penn Dept Bioengn & N

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs are becoming larger-scale and increasingly more accurate, and prescribe methods for building DNNs that can reliably model biological vision.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

引用

arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril LaBella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

π-HuB: the proteomic navigator of the human body

引用

NATURE 2024年第8042期636卷 322-331页

作者： He, Fuchu Aebersold, Ruedi Baker, Mark S. Bian, Xiuwu Bo, Xiaochen Chan, Daniel W. Chang, Cheng Chen, Luonan Chen, Xiangmei Chen, Yu-Ju Cheng, Heping Collins, Ben C. Corrales, Fernando Cox, Juergen Weinan, E. Van Eyk, Jennifer E. Fan, Jia Faridi, Pouya Figeys, Daniel Gao, George Fu Gao, Wen Gao, Zu-Hua Goda, Keisuke Goh, Wilson Wen Bin Gu, Dongfeng Guo, Changjiang Guo, Tiannan He, Yuezhong Heck, Albert J. R. Hermjakob, Henning Hunter, Tony Iyer, Narayanan Gopalakrishna Jiang, Ying Jimenez, Connie R. Joshi, Lokesh Kelleher, Neil L. Li, Ming Li, Yang Lin, Qingsong Liu, Cui Hua Liu, Fan Liu, Guang-Hui Liu, Yansheng Liu, Zhihua Low, Teck Yew Lu, Ben Mann, Matthias Meng, Anming Moritz, Robert L. Nice, Edouard Ning, Guang Omenn, Gilbert S. Overall, Christopher M. Palmisano, Giuseppe Peng, Yaojin Pineau, Charles Poon, Terence Chuen Wai Purcell, Anthony W. Qiao, Jie Reddel, Roger R. Robinson, Phillip J. Roncada, Paola Sander, Chris Sha, Jiahao Song, Erwei Srivastava, Sanjeeva Sun, Aihua Sze, Siu Kwan Tang, Chao Tang, Liujun Tian, Ruijun Vizcaino, Juan Antonio Wang, Chanjuan Wang, Chen Wang, Xiaowen Wang, Xinxing Wang, Yan Weiss, Tobias Wilhelm, Mathias Winkler, Robert Wollscheid, Bernd Wong, Limsoon Xie, Linhai Xie, Wei Xu, Tao Xu, Tianhao Yan, Liying Yang, Jing Yang, Xiao Yates, John Yun, Tao Zhai, Qiwei Zhang, Bing Zhang, Hui Zhang, Lihua Zhang, Lingqiang Zhang, Pingwen Zhang, Yukui Zheng, Yu Zi Zhong, Qing Zhu, Yunping Tan, Chris Soon Heng Gao, Mingxia Gao, Qiang Gao, Yushun Guo, Xuejiang He, Jie He, Jun He, Qing-Yu Hou, Jinlin Huang, Canhua Jia, Chenxi Kuster, Bernhard Li, Chaoying Li, Dong Li, Yanchang Liu, Siqi Liu, Xiaonan Liu, Ya Liu, Zhongyang Lu, Haojie Nie, Yongzhan Okada, Mariko Qian, Guojun Qin, Hongqiang Rao, Yu Rao, Zihe Ren, Xianwen Ren, Yan Shen, Feng Shen, Lin Su, Shicheng Tan, Minjia Tang, Ben Zhong Tao, Sheng-Ce Wang, Jian Wang, Tong Wei, Liming Wong, Catherine C. L. Xie, Xiaoliang Sunney Xu, Li Xu, Ping Yang, Huanming Yang, Jianjun Ye, Mingliang Ying, Wantao Yu, Xiaobo Yuan, Yaxiang Zeng, Qingcun Zha Beijing Inst Life Beijing Proteome Res Ctr Natl Ctr Prot Sci Beijing State Key Lab Med Prote Beijing Peoples R China Int Acad Phronesis Med Guangdong Guangzhou Guangdong Peoples R China Swiss Fed Inst Technol Inst Mol Syst Biol Dept Biol Zurich Switzerland Macquarie Univ Macquarie Med Sch Sydney NSW Australia Army Med Univ Mil Med Univ 3 Southwest Hosp Inst Pathol Chongqing Peoples R China Army Med Univ Mil Med Univ 3 Southwest Hosp Southwest Canc Ctr Chongqing Peoples R China Minist Educ China Key Lab Tumor Immunopathol Chongqing Peoples R China Inst Hlth Serv & Transfus Med Beijing Peoples R China Johns Hopkins Univ Dept Pathol Baltimore MD USA Johns Hopkins Univ Sidney Kimmel Comprehens Canc Ctr Baltimore MD USA Chinese Acad Sci Shanghai Inst Biochem & Cell Biol Ctr Excellence Mol Cell Sci Key Lab Syst Biol Shanghai Peoples R China Chinese Peoples Liberat Chinese PLA Gen Hosp Natl Clin Res Ctr Kidney DisState Key Lab Kidney Dept NephrolMed Ctr 1Nephrol InstBeijing Key L Beijing Peoples R China Acad Sinica Inst Chem Taipei Taiwan Peking Univ Peking Tsinghua Ctr Life SciColl Future Technol State Key Lab Membrane BiolInst Mol Med Natl Biomed Imaging CtrColl Future Technol Beijing Peoples R China Queens Univ Belfast Sch Biol Sci Belfast Antrim North Ireland CSIC Funct Prote Lab Ctr Nacl Biotecnol Madrid Spain Max Planck Inst Biochem Computat Syst Biochem Res Grp Martinsried Germany AI Sci Inst Beijing Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China Cedars Sinai Med Ctr Adv Clin Biosyst Res Inst Los Angeles CA 90048 USA Fudan Univ Liver Canc Inst Dept Liver Surg & TransplantatZhongshan Hosp Key Lab Carcinogenesis & Canc InvasMinist Educ Shanghai Peoples R China Hudson Inst Med Res Ctr Canc Res Clayton Vic Australia Monash Univ Sch Clin Sci Dept Med Monash Prote & Metabol Platform Clayton Vic Australia Univ Ottawa Sch Pharmaceut Sci Fac Med Ottawa ON Canada Univ Ottawa Dept Biochem Mic

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called pi-HuB (proteomic navigator of the human body). The aim of the pi-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology;(2) facilitate disease risk assessment and diagnosis;(3) uncover new drug targets;(4) optimize appropriate therapeutic strategies;and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

引用

PLOS COMPUTATIONAL BIOLOGY 2016年第7期12卷 e1004791页

作者： Gao, Chuan McDowell, Ian C. Zhao, Shiwen Brown, Christopher D. Engelhardt, Barbara E. Duke Univ Dept Stat Sci Durham NC USA Duke Univ Program Computat Biol & Bioinformat Durham NC USA Univ Penn Dept Genet Philadelphia PA 19104 USA Princeton Univ Dept Comp Sci Ctr Stat & Machine Learning Princeton NJ 08544 USA

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are coregulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER-samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

关键词： Genetic networks Gene expression Covariance Breast cancer Centrality Simulation and modeling Arteries Gene regulation

来源：评论

学校读者我要写书评

暂无评论

Quantum computing at the frontiers of biological sciences

arXiv

引用

arXiv 2019年

作者： Emani, Prashant S. Warrell, Jonathan Anticevic, Alan Bekiranov, Stefan Gandal, Michael McConnell, Michael J. Sapiro, Guillermo Aspuru-Guzik, Alán Baker, Justin Bastiani, Matteo McClure, Patrick Murray, John Sotiropoulos, Stamatios N. Taylor, Jacob Senthil, Geetha Lehner, Thomas Gerstein, Mark B. Harrow, Aram W. Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Yale School of Medicine Department of Psychiatry New HavenCT06511 United States University of Virginia School of Medicine Department of Biochemistry and Molecular Genetics CharlottesvilleVA22903 United States Department of Psychiatry Semel Institute David Geffen School of Medicine University of California–Los Angeles 695 Charles E. Young Drive South Los AngelesCA90095 United States Univ. of Virginia School of Medicine Department of Neuroscience CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Chemistry University of Toronto 80 St. George Street TorontoONM5S 3H6 Canada TorontoONM5S 1M1 Canada CIFAR Artificial Intelligence Research Chair Vector Institute TorontoONM5S 1M1 Canada Department of Computer Science University of Toronto 40 St. George Street TorontoONM5S 2E4 Canada Schizophrenia and Bipolar Disorder Program McLean Hospital BelmontMA02478 United States Department of Psychiatry Harvard Medical School BostonMA02114 United States Sir Peter Mansfield Imaging Centre School of Medicine University of Nottingham NottinghamNG7 2RD United Kingdom Machine Learning Team National Institute of Mental Health BethesdaMD20892 United States Section on Functional Imaging Methods National Institute of Mental Health BethesdaMD20892 United States Department of Psychiatry Yale University School of Medicine New HavenCT06511 United States Department of Physics Yale University New HavenCT06511 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States National Institute of Standards and Technology GaithersburgMD20899 United States Office of Genomics Research Coordination N

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Measuring the predictability of life outcomes with a scientific mass collaboration

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2020年第15期117卷 8398-8403页

作者： Salganik, Matthew J. Lundberg, Ian Kindel, Alexander T. Ahearn, Caitlin E. Al-Ghoneim, Khaled Almaatouq, Abdullah Altschul, Drew M. Brand, Jennie E. Carnegie, Nicole Bohme Compton, Ryan James Datta, Debanjan Davidson, Thomas Filippova, Anna Gilroy, Connor Goode, Brian J. Jahani, Eaman Kashyap, Ridhi Kirchner, Antje McKay, Stephen Morgan, Allison C. Pentland, Alex Polimis, Kivan Raes, Louis Rigobon, Daniel E. Roberts, Claudia V. Stanescu, Diana M. Suhara, Yoshihiko Usmania, Adaner Wang, Erik H. Adem, Muna Alhajri, Abdulla AlShebli, Bedoor Amin, Redwane Amos, Ryan B. Argyle, Lisa P. Baer-Bositis, Livia Buechi, Moritz Chung, Bo-Ryehn Eggert, William Faletto, Gregory Fan, Zhilin Freese, Jeremy Gadgil, Tejomay Gagne, Josh Gao, Yue Halpern-Manners, Andrew Hashim, Sonia P. Hausen, Sonia He, Guanhua Higuera, Kimberly Hogan, Bernie Horwitz, Ilana M. Hummel, Lisa M. Jain, Naman Jin, Kun Jurgens, David Kaminski, Patrick Karapetyan, Areg Kim, E. H. Leizman, Ben Liu, Naijia Moeser, Malte Mack, Andrew E. Mahajan, Mayank Mandell, Noah Marahrens, Helge Mercado-Garcia, Diana Mocz, Viola Mueller-Gastell, Katariina Musse, Ahmed Niu, Qiankun Nowak, William Omidvar, Hamidreza Or, Andrew Ouyang, Karen Pinto, Katy M. Porter, Ethan Porter, Kristin E. Qian, Crystal Rauf, Tamkinat Sargsyan, Anahit Schaffner, Thomas Schnabel, Landon Schonfeld, Bryan Sender, Ben Tang, Jonathan D. Tsurkov, Emma van Loon, Austin Varol, Onur Wang, Xiafei Wang, Zhi Wang, Julia Wang, Flora Weissman, Samantha Whitaker, Kirstie Wolters, Maria K. Woo, Wei Lee Wu, James Wu, Catherine Yang, Kengran Yin, Jingwen Zhao, Bingyu Zhu, Chenyun Brooks-Gunn, Jeanne Engelhardt, Barbara E. Hardt, Moritz Knox, Dean Levy, Karen Narayanan, Arvind Stewart, Brandon M. Watta, Duncan J. McLanahan, Sara Princeton Univ Dept Sociol Princeton NJ 08544 USA Univ Calif Los Angeles Dept Sociol Los Angeles CA 90095 USA Hawaz Riyadh 12363 Saudi Arabia MIT Sloan Sch Management Cambridge MA 02142 USA MIT Media Lab Cambridge MA 02139 USA Univ Edinburgh Dept Psychol Mental Hlth Data Sci Scotland Edinburgh EH8 9JZ Midlothian Scotland Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Montana State Univ Dept Math Sci Bozeman MT 59717 USA Univ Calif Santa Cruz Human Comp Interact Lab Santa Cruz CA 95064 USA Virginia Polytech Inst & State Univ Discovery Analyt Ctr Arlington VA 22203 USA Cornell Univ Dept Sociol Uris Hall Ithaca NY 14853 USA GitHub San Francisco CA 94107 USA Univ Washington Dept Sociol Seattle WA 98105 USA Virginia Polytech Inst & State Univ Fralin Life Sci Inst Social & Decis Analyt Lab Arlington VA 22203 USA MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA Univ Oxford Dept Sociol Oxford OX1 1JD England Univ Oxford Nuffield Coll Oxford OX1 1NF England Univ Oxford Sch Anthropol Oxford OX2 6PE England Univ Oxford Museum Ethnog Oxford OX2 6PE England RTI Int Survey Res Div Program Res Survey Methodol Res Triangle Pk NC 27709 USA Univ Lincoln Sch Social & Polit Sci Lincoln LN6 7TS England Univ Colorado Dept Comp Sci Boulder CO 80309 USA Univ Washington Ctr Study Demog & Ecol Seattle WA 98105 USA Tilburg Univ Tilburg Sch Econ & Management Dept Econ NL-5037 AB Tilburg Netherlands Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Dept Polit Princeton NJ 08544 USA Harvard Univ Dept Sociol Cambridge MA 02138 USA Indiana Univ Dept Sociol Bloomington IN 47405 USA MIT Dept Nucl Sci & Engn 77 Massachusetts Ave Cambridge MA 02139 USA New York Univ Abu Dhabi Social Sci Div Computat Social Sci Lab Abu Dhabi 129188 U Arab Emirates Princeton Univ Bendheim Ctr Finance Princeton NJ 08544 USA Brigham Young Univ Dept P

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method;160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

关键词： life course prediction machine learning mass collaboration

来源：评论

学校读者我要写书评

暂无评论

Bayesian group factor analysis with structured sparsity

引用

JOURNAL OF machine learning RESEARCH 2016年 17卷 1-47页

作者： Zhao, Shiwen Gao, Chuan Mukherjee, Sayan Engelhardt, Barbara E. Duke Univ Dept Stat Sci Computat Biol & Bioinformat Program Durham NC 27708 USA Duke Univ Dept Stat Sci Durham NC 27708 USA Duke Univ Dept Stat Sci Comp Sci Math Durham NC 27708 USA Princeton Univ Dept Comp Sci Ctr Stat & Machine Learning Princeton NJ 08540 USA

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices;in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects;ii) the ability to scale naturally to large numbers of observations;iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained;and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： Bayesian structured sparsity canonical correlation analysis sparse priors sparse and low-rank matrix decomposition mixture models parameter expansion

来源：评论

学校读者我要写书评

暂无评论

Towards Life-Long Mobility: Accessible Transportation with Automation 8

Towards Life-Long Mobility: Accessible Transportation with A...

引用

8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI)

作者： Jeon, Myounghoon Sutter, Christine Politis, Ioannis Terken, Jacques M. B. Shladover, Steven E. Poppinga, Benjamin Michigan Technol Univ Mind Mus Machine Lab Cognit & Learning Sci Comp Sci Houghton MI 49931 USA Tech Univ Darmstadt Inst Ergon & Human Factors Ergon & Syst Design Mech Engn Darmstadt Germany Univ Cambridge Dept Engn Engn Design Ctr Cambridge CB2 1PZ England Tech Univ Eindhoven Dept Ind Design Ctr Engn Sect User Eindhoven Netherlands Univ Calif Berkeley Inst Transportat Studies Calif PATH Program Richmond CA 94804 USA Smarttent Syst Ingolstadt Germany

ISBN: (纸本)9781450346542

Despite the prevalent discussions on automated vehicles, little research has been conducted with a focus on inclusiveness of traditionally excluded populations from driving. Even though we may envision a future where everyone can drive with perfect automation, the problem will not be that simple. As with any other problem domains, we need to scrutinize all the design considerations-not only each population's characteristics ( capabilities and limitations), but also the entire system, technological limitations, and task environments. To this end, the present paper explores challenges and opportunities of automated vehicles for multiple populations, including people with various difficulties/disabilities, older adults, and children. This paper brings up some controversial points and is expected to promote lively discussions at the conference.

关键词： Accessible computing assistive technology automated vehicles inclusive design

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：