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Protecting scientific integrity in an age of generative AI

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第22期121卷 e2407886121-e2407886121页

作者： Blau, Wolfgang Cerf, Vinton G. Enriquez, Juan Francisco, Joseph S. Gasser, Urs Gray, Mary L. Greaves, Mark Grosz, Barbara J. Jamieson, Kathleen Hall Haug, Gerald H. Hennessy, John L. Horvitz, Eric Kaiser, David I. London, Alex John Lovell-Badge, Robin McNutt, Marcia K. Minow, Martha Mitchell, Tom M. Ness, Susan Parthasarathy, Shobita Perlmutter, Saul Press, William H. Wing, Jeannette M. Witherell, Michael Brunswick Grp London WC2A 3ED England Google LLC Google Global Networking Reston VA 20190 USA Excel Venture Management Boston MA 02116 USA Univ Penn Dept Earth & Environm Sci Philadelphia PA 19104 USA Univ Penn Dept Chem Philadelphia PA 19104 USA Tech Univ Munich Sch Social Sci & Technol D-80333 Munich Germany Microsoft Res New England Lab Cambridge MA 02142 USA Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN 47408 USA Schmidt Sci Washington DC 20008 USA Harvard Univ Harvard Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA Univ Penn Annenberg Publ Policy Ctr Philadelphia PA 19104 USA German Natl Acad Sci Leopoldina D-06108 Halle Germany Stanford Univ Knight Hennessy Scholars Stanford CA 94305 USA Off Chief Sci Officer Microsoft Redmond WA 98052 USA MIT Program Sci Technol & Soc Cambridge MA 02139 USA MIT Dept Phys Cambridge MA 02139 USA Carnegie Mellon Univ Dept Philosophy Pittsburgh PA 15213 USA Francis Crick Inst Lab Stem Cell Biol & Dev Genet London NW1 1AT England Natl Acad Sci Washington DC 20001 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Michigan Sci Technol & Publ Policy Program Ann Arbor MI 48109 USA Univ Calif Berkeley Dept Phys Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Phys Div Berkeley CA 94720 USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Columbia Univ Res & Comp Sci Dept New York NY 10027 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

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Longitudinal clinical and functional outcome in distinct cognitive subgroups of first-episode psychosis: a cluster analysis

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PSYCHOLOGICAL MEDICINE 2023年第6期53卷 2317-2327页

作者： Oomen, Priscilla P. Begemann, Marieke J. H. Brand, Bodyl A. de Haan, Lieuwe Veling, Wim Koops, Sanne van Os, Jim Smit, Filip Bakker, P. Roberto van Beveren, Nico Boonstra, Nynke Guloksuz, Sinan Kikkert, Martijn Lokkerbol, Joran Marcelis, Machteld Rosema, Bram-Sieben de Beer, Franciska Gangadin, Shiral S. Geraets, Chris N. W. van't Hag, Erna Haveman, Yudith van der Heijden, Inge Voppel, Alban E. Willemse, Elske van Amelsvoort, Therese Bak, Maarten Batalla, Albert Been, Agaath van den Bosch, Marinte van den Brink, Truus Faber, Gunnar Grootens, Koen P. de Jonge, Martin Knegtering, Rikus Kurkamp, Jorg Mahabir, Amrita Pijnenborg, Gerdina H. M. Staring, Tonnie Veen, Natalie Veerman, Selene Wiersma, Sybren Graveland, Ellen Hoornaar, Joelle Sommer, Iris E. C. Univ Groningen Univ Med Ctr Groningen Dept Biomed Sci Cells & Syst Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Amsterdam UMC Dept Early Psychosis Acad Med Ctr Amsterdam Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Univ Med Ctr Utrecht Dept Psychiat UMC Utrecht Brain Ctr Utrecht Netherlands Maastricht Univ Sch Mental Hlth & Neurosci MheNS Dept Psychiat & Neuropsychol Med Ctr Maastricht Netherlands Kings Coll London Kings Hlth Partners Dept Psychosis Studies Inst Psychiat Psychol & Neurosci London England Univ Amsterdam Amsterdam Publ Hlth Res Inst Dept Epidemiol & Biostat Med Ctr Locat VUmc Amsterdam Netherlands Vrije Univ Amsterdam Publ Hlth Res Inst Dept Clin Neuro & Dev Psychol Amsterdam Netherlands Trimbos Inst Netherlands Inst Mental Hlth Ctr Econ Evaluat & Machine Learning Utrecht Netherlands Arkin Mental Hlth Care Dept Res Amsterdam Netherlands Antes Ctr Mental Hlth Care Rotterdam Netherlands Erasmus MC Dept Neurosci Rotterdam Netherlands Erasmus MC Dept Psychiat Rotterdam Netherlands Univ Appl Sci NHL Stenden Leeuwarden Netherlands KieN VIP Mental Hlth Care Serv Leeuwarden Netherlands Yale Univ Dept Psychiat Sch Med New Haven CT USA Maastricht Univ Med Ctr Sch Mental Hlth & Neurosci Dept Psychiat & Neuropsychol EURON Maastricht Netherlands Inst Mental Hlth Care Eindhoven GGzE Eindhoven Netherlands Janssen Cilag BV Breda Netherlands Mondriaan Mental Hlth Care Heerlen Netherlands Dimence Inst Mental Hlth Deventer Netherlands GGZ Centraal Early Intervent Team Amersfoort Netherlands Mental Hlth Inst Yulius Dordrecht Netherlands Reinier Arkel Inst Mental Hlth Care sHertogenbosch Netherlands Pro Persona Mental Hlth Program Psychosis & Severe Mental Illness Wolfheze Netherlands Lentis Psychiat Inst Lentis Res Groningen Netherlands Mediant ABC Twente Ctr Youth Psychosis Enschede Netherlands GGNet Early Psychosis Team Apeldoorn N

Background Cognitive deficits may be characteristic for only a subgroup of first-episode psychosis (FEP) and the link with clinical and functional outcomes is less profound than previously thought. This study aimed to identify cognitive subgroups in a large sample of FEP using a clustering approach with healthy controls as a reference group, subsequently linking cognitive subgroups to clinical and functional outcomes. Methods 204 FEP patients were included. Hierarchical cluster analysis was performed using baseline brief assessment of cognition in schizophrenia (BACS). Cognitive subgroups were compared to 40 controls and linked to longitudinal clinical and functional outcomes (PANSS, GAF, self-reported WHODAS 2.0) up to 12-month follow-up. Results Three distinct cognitive clusters emerged: relative to controls, we found one cluster with preserved cognition (n = 76), one moderately impaired cluster (n = 74) and one severely impaired cluster (n = 54). Patients with severely impaired cognition had more severe clinical symptoms at baseline, 6- and 12-month follow-up as compared to patients with preserved cognition. General functioning (GAF) in the severely impaired cluster was significantly lower than in those with preserved cognition at baseline and showed trend-level effects at 6- and 12-month follow-up. No significant differences in self-reported functional outcome (WHODAS 2.0) were present. Conclusions Current results demonstrate the existence of three distinct cognitive subgroups, corresponding with clinical outcome at baseline, 6- and 12-month follow-up. Importantly, the cognitively preserved subgroup was larger than the severely impaired group. Early identification of discrete cognitive profiles can offer valuable information about the clinical outcome but may not be relevant in predicting self-reported functional outcomes.

关键词： Clustering cognition FEP functional outcome global functioning psychosis

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Reconstructed Single-Cell Fate Trajectories Define Lineage Plasticity Windows during Differentiation of Human PSC-Derived Distal Lung Progenitors

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CELL STEM CELL 2020年第4期26卷 593-+页

作者： Hurley, Killian Ding, Jun Villacorta-Martin, Carlos Herriges, Michael J. Jacob, Anjali Vedaie, Marall Alysandratos, Konstantinos D. Sun, Yuliang L. Lin, Chieh Werder, Rhiannon B. Huang, Jessie Wilson, Andrew A. Mithal, Aditya Mostoslavsky, Gustavo Oglesby, Irene Caballero, Ignacio S. Guttentag, Susan H. Ahangari, Farida Kaminski, Naftali Rodriguez-Fraticelli, Alejo Camargo, Fernando Bar-Joseph, Ziv Kotton, Darrell N. Boston Univ Ctr Regenerat Med Boston MA 02118 USA Boston Med Ctr Boston MA 02118 USA Boston Univ Pulm Ctr Sch Med Boston MA 02118 USA Boston Univ Dept Med Sch Med Boston MA 02118 USA Beaumont Hosp Royal Coll Surg Ireland Educ & Res Ctr Dept Med Dublin Ireland Royal Coll Surgeons Ireland Tissue Engn Res Grp Dublin Ireland Carnegie Mellon Univ Sch Comp Sci Computat Biol Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Sch Comp Sci Machine Learning Dept Pittsburgh PA 15217 USA Vanderbilt Univ Dept Pediat Monroe Carell Jr Childrens Hosp Nashville TN 37232 USA Yale Univ Pulm Crit Care & Sleep Med Sch Med New Haven CT 16520 USA Boston Childrens Hosp Stem Cell Program Boston MA 02115 USA Harvard Univ Dept Stem Cell & Regenerat Biol Cambridge MA 02138 USA Harvard Stem Cell Inst Boston MA 02115 USA

Alveolar epithelial type 2 cells (AEC2s) are the facultative progenitors responsible for maintaining lung alveoli throughout life but are difficult to isolate from patients. Here, we engineer AEC2s from human pluripotent stem cells (PSCs) in vitro and use time-series single-cell RNA sequencing with lentiviral bar-coding to profile the kinetics of their differentiation in comparison to primary fetal and adult AEC2 benchmarks. We observe bifurcating cell-fate trajectories as primordial lung progenitors differentiate in vitro, with some progeny reaching their AEC2 fate target, while others diverge to alternative non-lung endo-dermal fates. We develop a Continuous State Hidden-Markov model to identify the timing and type of signals, such as overexuberant Wnt responses, that induce some early multipotent NKX2-1(+) progenitors to lose lung fate. Finally, we find that this initial developmental plasticity is regulatable and subsides over time, ultimately resulting in PSC-derived AEC2s that exhibit a stable phenotype and nearly limitless self-renewal capacity.

关键词： Alveolar epithelial type 2 cells induced pluripotent stem cells lungprogenitors directed differentiation cell fate cellular plasticity heterogeneity single cell RNA sequencing DNA barcoding hidden Markov model

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Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior

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NATURE COMMUNICATIONS 2020年第1期11卷 4014-4014页

作者： Li, Yuanning Ward, Michael J. Richardson, R. Mark G'Sell, Max Ghuman, Avniel Singh Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA 15260 USA Carnegie Mellon Univ Program Neural Computat & Machine Learning Pittsburgh PA 15213 USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA 15260 USA Massachusetts Gen Hosp Dept Neurosurg Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA Univ Calif San Francisco Dept Neurol Surg San Francisco CA 94143 USA

Perception reflects not only sensory inputs, but also the endogenous state when these inputs enter the brain. Prior studies show that endogenous neural states influence stimulus processing through non-specific, global mechanisms, such as spontaneous fluctuations of arousal. It is unclear if endogenous activity influences circuit and stimulus-specific processing and behavior as well. Here we use intracranial recordings from 30 pre-surgical epilepsy patients to show that patterns of endogenous activity are related to the strength of trial-by-trial neural tuning in different visual category-selective neural circuits. The same aspects of the endogenous activity that relate to tuning in a particular neural circuit also correlate to behavioral reaction times only for stimuli from the category that circuit is selective for. These results suggest that endogenous activity can modulate neural tuning and influence behavior in a circuit- and stimulus-specific manner, reflecting a potential mechanism by which endogenous neural states facilitate and bias perception. Endogenous brain states influence perception. In this manuscript the authors use human intracranial recordings to provide mechanistic insight into this process by showing that endogenous brain activity facilitates neural tuning and behavior in a stimulus and circuit specific manner.

关键词： Perception Neural decoding Object vision

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The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2021年第51期118卷 e2111454118-e2111454118页

作者： Salomon, Joshua A. Reinhart, Alex Bilinski, Alyssa Chua, Eu Jing La Motte-Kerr, Wichada Ronn, Minttu M. Reitsma, Marissa B. Morris, Katherine A. LaRocca, Sarah Farag, Tamer H. Kreuter, Frauke Rosenfeld, Roni Tibshirani, Ryan J. Stanford Univ Dept Hlth Policy Stanford CA 94305 USA Stanford Univ Freeman Spogli Inst Int Studies Stanford CA 94305 USA Carnegie Mellon Univ Dept Stat & Data Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Delphi Grp Pittsburgh PA 15213 USA Brown Sch Publ Hlth Dept Hlth Serv Policy & Practice Providence RI 02903 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Harvard TH Chan Sch Publ Hlth Dept Global Hlth & Populat Boston MA 02115 USA Meta Demog & Survey Sci Menlo Pk CA 94025 USA Meta Hlth Partnerships Menlo Pk CA 94025 USA Univ Maryland Joint Program Survey Methodol College Pk MD 20742 USA Ludwig Maximilians Univ Munchen Dept Stat D-80539 Munich Germany

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey-over 20 million responses in its first year of operation-allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

关键词： COVID-19 SARS-CoV2 survey

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Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2018年第Jul.期89卷 61-69页

作者： da Costa, Cristiano Andre Pasluosta, Cristian F. Eskofier, Bjorn da Silva, Denise Bandeira Righi, Rodrigo da Rosa Univ Vale Rio Sinos UNISINOS Appl Comp Grad Program Software Innovat Lab SOFTWARELAB BR-93022750 Sao Leopoldo Brazil Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Comp Sci Machine Learning & Data Analyt Lab D-91058 Erlangen Germany Univ Freiburg Dept Microsyst Engn IMTEK Lab Biomed Microtechnol Georges Koehler Allee 102 D-79110 Freiburg Germany

Background: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. Methods: This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. Results: As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. Conclusion: It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.

关键词： Vital signs Early Warning Score Wireless sensor networks machine learning Health records Internet of Things

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Bee-Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-20页

作者： Masutti, Thiago A. S. de Castro, Leandro N. Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab LCoN Grad Program Elect Engn & Comp R Consolacao 930 BR-01302000 Sao Paulo SP Brazil

Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

关键词： BEES algorithm VEHICLE routing problem MATHEMATICAL optimization PHYSICAL distribution of goods PROBLEM solving

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Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

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RADIOTHERAPY AND ONCOLOGY 2024年 197卷 110345-110345页

作者： Hurkmans, Coen Bibault, Jean -Emmanuel Brock, Kristy K. van Elmpt, Wouter Feng, Mary Fuller, Clifton David Jereczek-Fossa, Barbara A. Korreman, Stine Landry, Guillaume Madesta, Frederic Mayo, Chuck Mcwilliam, Alan Moura, Filipe Muren, Ludvig P. El Naqa, Issam Seuntjens, Jan Valentini, Vincenzo Velec, Michael Catharina Hosp Dept Radiat Oncol Michelangelolaan 2POB 1350 NL-5602 ZA Eindhoven Netherlands Tech Univ Eindhoven Dept Elect Engn Eindhoven Netherlands Georges Pompidou European Hosp Dept Radiat Oncol Paris France Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX USA Univ Texas MD Anderson Canc Ctr Dept Radiat Phys Houston TX USA Maastricht Univ Med Ctr GROW Sch Oncol & Reprod Dept Radiat Oncol MAASTRO Maastricht Netherlands Univ Calif San Francisco San Francisco CA USA Univ Texas MD Anderson Canc Dept Radiat Oncol Houston TX USA Univ Milan Dept Oncol & Hematooncol Milan Italy IEO European Inst Oncol IRCCS Dept Radiat Oncol Milan Italy Aarhus Univ Dept Clin Med Aarhus Denmark Aarhus Univ Hosp Danish Ctr Particle Therapy Aarhus Denmark Ludwig Maximilians Univ Munchen LMU Univ Hosp Dept Radiat Oncol Munich Germany German Canc Consortium DKTK Partner Site Munich Tubingen Germany Bavarian Canc Res Ctr BZKF Partner Site Munich Munich Germany Univ Med Ctr Hamburg Eppendorf Dept Computat Neurosci Hamburg Germany Univ Med Ctr Hamburg Eppendorf Inst Appl Med Informat Hamburg Germany Univ Med Ctr Hamburg Eppendorf Ctr Biomed Artificial Intelligence bAIome Hamburg Germany Univ Michigan Inst Healthcare Policy & Innovat Ann Arbor MI USA Univ Manchester Div Canc Sci Manchester England Portuguese Red Cross Higher Hlth Sch Lisbon CrossI&D Lisbon Res Ctr Lisbon Portugal H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning Tampa FL 33612 USA Univ Hlth Network Princess Margaret Canc Ctr Radiat Med Program Toronto ON Canada Univ Toronto Dept Radiat Oncol Toronto ON Canada Univ Toronto Dept Med Biophys Toronto ON Canada Fdn Policlin Univ Agostino Gemelli IRCCS Dept Diagnost Imaging Oncol Radiotherapy & Hematol Rome Italy Univ Cattolica Sacro Cuore Rome Italy Univ Toronto Princess Margaret Canc Ctr Radiat Med Program Toronto ON Canada Univ Toronto Dept Radiat Oncol Toronto ON Canada

Background and purpose: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.

关键词： Artificial Intelligence Deep learning machine learning

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Clustering gene expression time series data using an infinite Gaussian process mixture model

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PLOS COMPUTATIONAL BIOLOGY 2018年第1期14卷 e1005896-e1005896页

作者： McDowell, Ian C. Manandhar, Dinesh Vockley, Christopher M. Schmid, Amy K. Reddy, Timothy E. Engelhardt, Barbara E. Duke Univ Computat Biol & Bioinformat Grad Program Durham NC 27708 USA Duke Univ Ctr Genom & Computat Biol Durham NC 27708 USA Duke Univ Med Ctr Dept Biostat & Bioinformat Durham NC 27708 USA Duke Univ Dept Biol Durham NC USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA

Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://***/PrincetonUniversity/DP_GP_cluster.

关键词： GENE expression TIME series analysis GAUSSIAN processes ECOLOGICAL disturbances COMPUTATIONAL biology

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