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Advancing precision care in pregnancy through a treatable fetal findings list

American Journal of Human Genetics 2025年第6期112卷 1251-1269页

作者： Cohen, Jennifer L. Duyzend, Michael Adelson, Sophia M. Yeo, Julie Fleming, Mark Ganetzky, Rebecca Hale, Rebecca Mitchell, Deborah M. Morton, Sarah U. Reimers, Rebecca Roberts, Amy Strong, Alanna Tan, Weizhen Thiagarajah, Jay R. Walker, Melissa A. Green, Robert C. Gold, Nina B. Department of Pediatrics Division of Medical Genetics Duke University Durham NC United States Department of Pediatrics Division of Genetics and Genomics Boston Children's Hospital and Harvard Medical School Boston MA United States Center for Genomic Medicine Massachusetts General Hospital Boston MA United States Program in Medical and Population Genetics The Broad Institute of MIT and Harvard Cambridge MA United States Brigham and Women's Hospital Boston MA United States Stanford School of Medicine Stanford CA United States Department of Pediatrics Massachusetts General Hospital Boston MA United States Department of Pathology Boston Children's Hospital and Harvard Medical School Boston MA United States Center for Computational Genomic Medicine and Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA United States Department of Pediatrics Perelman School of Medicine Philadelphia PA United States Department of Pediatrics Division of Immunology Boston Children's Hospital and Harvard Medical School Boston MA United States Division of Pediatric Endocrinology Massachusetts General Hospital Boston MA United States Department of Pediatrics Division of Newborn Medicine Boston Children's Hospital Boston MA United States Department of Pediatrics Harvard Medical School Boston MA United States Rady Children's Institute for Genomic Medicine Rady Children's Hospital Division of Perinatology Scripps Research Translational Institute University of California San Diego San Diego CA United States Department of Cardiology Division of Genetics and Genomics Department of Pediatrics Boston Children's Hospital Boston MA United States Division of Human Genetics Children's Hospital of Philadelphia Philadelphia PA United States Center for Applied Genomics Children's Hospital of Philadelphia Philadelphia PA United States Division of Pediatric Nephrology Massachusetts General Hospital for Children Boston MA United States Division of Ga

The use of genomic sequencing (GS) for prenatal diagnosis of fetuses with sonographic abnormalities has grown tremendously over the past decade. Fetal GS also offers an opportunity to identify incidental genomic variants that are unrelated to the fetal phenotype but may be relevant to fetal and newborn health. There are currently no guidelines for reporting incidental findings from fetal GS. In the United States, GS for adults and children is recommended to include a list of “secondary findings” genes (ACMG SF v.3.2) that are associated with disorders for which surveillance or treatment can reduce morbidity and mortality. The genes on ACMG SF v.3.2 predominantly cause adult-onset disorders. Importantly, many genetic disorders with fetal and infantile onset are treatable as well. A proposed solution is to create a “treatable fetal findings list,” which can be offered to pregnant individuals undergoing fetal GS or, eventually, as a standalone cell-free fetal DNA screening test. In this integrative review, we propose criteria for a treatable fetal findings list, then identify genetic disorders with clinically available or emerging fetal interventions and those for which clinical detection and intervention in the first week of life might lead to improved outcomes. Finally, we synthesize the potential benefits, limitations, and risks of a treatable fetal findings list. © 2025 American Society of Human Genetics

关键词： actionable findings genomic sequencing prenatal diagnosis prenatal therapies

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On the Parameter Combinations that Matter and on Those that do Not

arXiv

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arXiv 2021年

作者： Evangelou, Nikolaos Wichrowski, Noah J. Kevrekidis, George A. Dietrich, Felix Kooshkbaghi, Mahdi McFann, Sarah Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Mathematics and Statistics University of Massachusetts Amherst United States Department of Informatics Technical University of Munich Germany The Program in Applied and Computational Mathematics Princeton University United States Department of Chemical and Biological Engineering Princeton University United States Lewis-Sigler Institute for Integrative Genomics Princeton University United States

We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established *** Codes 37E99 (Primary), 68T07 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

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Immunogenomic Landscape of Hematological Malignancies (vol 38, pg 380, 2020)

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CANCER CELL 2020年第3期38卷 424-428页

作者： Dufva, Olli Polonen, Petri Bruck, Oscar Keranen, Mikko A. I. Klievink, Jay Mehtonen, Juha Huuhtanen, Jani Kumar, Ashwini Malani, Disha Siitonen, Sanna Kankainen, Matti Ghimire, Bishwa Lahtela, Jenni Mattila, Pirkko Vaha-Koskela, Markus Wennerberg, Krister Granberg, Kirsi Leivonen, Suvi-Katri Meriranta, Leo Heckman, Caroline Leppa, Sirpa Nykter, Matti Lohi, Olli Heinaniemi, Merja Mustjoki, Satu Hematology Research Unit Helsinki Helsinki University Hospital Comprehensive Cancer Center (HUH CCC) 00029 Helsinki Finland Translational Immunology Research Program and Department of Clinical Chemistry and Hematology University of Helsinki (UH) 00029 Helsinki Finland iCAN Digital Precision Cancer Medicine Flagship Helsinki Finland Institute of Biomedicine School of Medicine University of Eastern Finland 70211 Kuopio Finland Institute for Molecular Medicine Finland UH 00014 Helsinki Finland Department of Clinical Chemistry UH and HUSLAB HUH 00029 Helsinki Finland Laboratory of Computational Biology Faculty of Medicine and Health Technology Tampere University (TU) 33014 Tampere Finland Department of Oncology HUH CCC 00029 Helsinki Finland Applied Tumor Genomics Research Program Faculty of Medicine UH 00014 Helsinki Finland Tampere Center for Child Health Research TU and Tays Cancer Center Tampere University Hospital 33521 Tampere Finland

Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. We integrated over 8,000 transcriptomes and 2,000 samples with multilevel genomics of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival, and validated key findings experimentally. Infiltration of cytotoxic lymphocytes was associated with TP53 and myelodysplasia-related changes in acute myeloid leukemia, and activated B cell-like phenotype and interferon-γ response in lymphoma. CIITA methylation regulating antigen presentation, cancer type-specific immune checkpoints, such as VISTA in myeloid malignancies, and variation in cancer antigen expression further contributed to immune heterogeneity and predicted survival. Our study provides a resource linking immunology with cancer subtypes and genomics in hematological malignancies.

关键词： cancer immunology immunotherapy hematology genomics epigenomics leukemia lymphoma immune checkpoint antigen presentation

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Weight, habitual fibre intake, and microbiome composition predict tolerance to fructan supplementation

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Journal of Plant Foods 2024年第6期75卷

作者： Jeffrey Letourneau Benjamin C. Neubert Diana Dayal Verónica M. Carrion Heather K. Durand Eric P. Dallow Sharon Jiang Michelle Kirtley Geoffrey S. Ginsburg P. Murali Doraiswamy Lawrence A. David a Department of Molecular Genetics and Microbiology Duke University School of Medicine Durham NC USA b Program in Computational Biology and Bioinformatics Duke University School of Medicine Durham NC USA c School of Medicine University of North Carolina Chapel Hill NC USA d Duke Office of Clinical Research Duke University Durham NC USA e Duke Center for Applied Genomics and Precision Medicine Duke University Health System Durham NC USAf Department of Medicine Duke University School of Medicine Durham NC USA e Duke Center for Applied Genomics and Precision Medicine Duke University Health System Durham NC USAf Department of Medicine Duke University School of Medicine Durham NC USAg Department of Psychiatry Duke University School of Medicine Durham NC USAh Duke Microbiome Center Duke University School of Medicine Durham NC USA a Department of Molecular Genetics and Microbiology Duke University School of Medicine Durham NC USAb Program in Computational Biology and Bioinformatics Duke University School of Medicine Durham NC USAh Duke Microbiome Center Duke University School of Medicine Durham NC USA

Fructans are commonly used as dietary fibre supplements for their ability to promote the growth of beneficial gut microbes. However, fructan consumption has been associated with various dosage-dependent side effects. We characterised side effects in an exploratory analysis of a randomised trial in healthy adults (n= 40) who consumed 18 g/day inulin or placebo. We found that individuals weighing more or habitually consuming higher fibre exhibited the best tolerance. Furthermore, we identified associations between gut microbiome composition and host tolerance. Specifically, higher levels ofChristensenellaceae R-7 groupwere associated with gastrointestinal discomfort, and a machine-learning-based approach successfully predicted high levels of flatulence, with[Ruminococcus] torques groupand (Oscillospiraceae)UCG-002 *** as key predictive taxa. These data reveal trends that can help guide personalised recommendations for initial inulin dosage. Our results support prior ecological findings indicating that fibre supplementation has the greatest impact on individuals whose baseline fibre intake is lowest.

关键词： Microbiome inulin dietary fibre gastroenterology bioinformatics

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Causal models on probability spaces

arXiv

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arXiv 2019年

作者： Cabreros, Irineo Storey, John D. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States

We describe the interface between measure theoretic probability and causal inference by constructing causal models on probability spaces within the potential outcomes framework. We find that measure theory provides a precise and instructive language for causality and that consideration of the probability spaces underlying causal models offers clarity into central concepts of causal inference. By closely studying simple, instructive examples, we demonstrate insights into causal effects, causal interactions, matching procedures, and randomization. Additionally, we introduce a simple technique for visualizing causal models on probability spaces that is useful both for generating examples and developing causal intuition. Finally, we provide an axiomatic framework for causality and make initial steps towards a formal theory of general causal models. Copyright © 2019, The Authors. All rights reserved.

关键词： Probability

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

引用

arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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P805: Advancing precision care in pregnancy through a treatable fetal findings list

Genetics in Medicine Open

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Genetics in Medicine Open 2025年 3卷 103174-103174页

作者： Cohen, Jennifer Duyzend, Michael Adelson, Sophia Yeo, Julie Fleming, Mark Ganetzky, Rebecca Hale, Rebecca Mitchell, Deborah Morton, Sarah Reimers, Rebecca Roberts, Amy Strong, Alanna Tan, Weizhen Thiagarajah, Jay Walker, Melissa Green, Robert Gold, Nina Duke University Department of Pediatrics Division of Medical Genetics Durham NC Division of Genetics and Genomics Department of Pediatrics Boston Children's Hospital and Harvard Medical School Boston MA Center for Genomic Medicine Massachusetts General Hospital Boston MA Program in Medical and Population Genetics The Broad Institute of MIT and Harvard Cambridge MA Brigham and Women’s Hospital Stanford School of Medicine Massachusetts General Hospital Department of Pediatrics Department of Pathology Boston Children’s Hospital and Harvard Medical School Center for Computational Genomic Medicine & Division of Human Genetics Children’s Hospital of Philadelphia Philadelphia PA Department of Pediatrics Perelman School of Medicine Philadelphia PA Division of Immunology Department of Pediatrics Boston Children's Hospital and Harvard Medical School Boston MA Division of Pediatric Endocrinology Massachusetts General Hospital Boston MA Division of Newborn Medicine Department of Pediatrics Boston Children’s Hospital； Department of Pediatrics Harvard Medical School Rady Children’s Institute for Genomic Medicine Rady Children’s Hospital Division of Perinatology Scripps Research Translational Institute University of California San Diego Department of Pediatrics Harvard Medical School Boston MA Department of Cardiology and Division of Genetics and Genomics Department of Pediatrics Boston Children’s Hospital Boston MA Division of Human Genetics Children's Hospital of Philadelphia Center for Applied Genomics Children's Hospital of Philadelphia Department of Pediatrics Perelman School of Medicine at the University of Pennsylvania Department of Pediatrics Harvard Medical School Boston MA Division of Pediatric Nephrology Massachusetts General Hospital for Children Boston MA Division of Gastroenterology Hepatology and Nutrition Boston Children’s Hospital； Harvard Medical School Boston MA Department of Neurology Division of Pediatric Neurology Massachusetts General Hospital

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