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arXiv 2023年

作者： Zhang, Shiyun Jin, Weiwei Wang, Dong Xu, Ding Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics University of Science and Technology of China Anhui Hefei230026 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as hGi = (1 − F−)hG+i + F−hG−i, where F− is the fraction of jammed packings with negative shear moduli and hG+i and hG−i are the average values from packings with positive and negative moduli, respectively. We show that hG+i and h|G−|i obey different power-law scaling relations above and below pN2 ∼ 1. For pN2 > 1, both hG+iN and h|G−|iN ∼ (pN2)β, where β ∼ 0.5 for repulsive linear spring interactions. Despite this, hGiN ∼ (pN2)β0 with β0 & 0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Gamma distribution in the pN2 1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2 1 limit. We also partition jammed disk packings into subsystems using Delanunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G > 0. The spatial correlation function of local shear moduli C(~r) displays weak correlations for pn2sub −2, where nsub is the number of particles within each subsystem. However, C(~r) begins to develop long-ranged spatial correlations with four-fold angular symmetry for pn2sub & 10−2 © 2023, CC BY-NC-ND.

关键词： Elastic moduli

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A large-scale binding and functional map of human RNA-binding proteins (vol 583, pg 711, 2020)

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NATURE 2021年第7842期589卷 E5-E5页

作者： Van Nostrand, Eric L. Freese, Peter Pratt, Gabriel A. Wang, Xiaofeng Wei, Xintao Xiao, Rui Blue, Steven M. Chen, Jia-Yu Cody, Neal A. L. Dominguez, Daniel Olson, Sara Sundararaman, Balaji Zhan, Lijun Bazile, Cassandra Bouvrette, Louis Philip Benoit Bergalet, Julie Duff, Michael O. Garcia, Keri E. Gelboin-Burkhart, Chelsea Hochman, Myles Lambert, Nicole J. Li, Hairi McGurk, Michael P. Nguyen, Thai B. Palden, Tsultrim Rabano, Ines Sathe, Shashank Stanton, Rebecca Su, Amanda Wang, Ruth Yee, Brian A. Zhou, Bing Louie, Ashley L. Aigner, Stefan Fu, Xiang-Dong Lecuyer, Eric Burge, Christopher B. Graveley, Brenton R. Yeo, Gene W. Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Medical Research Institute Wuhan University Wuhan China Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA Institut de Recherches Cliniques de Montréal (IRCM) Montreal Quebec Canada Department of Biochemistry and Molecular Medicine Université de Montréal Montreal Quebec Canada Division of Experimental Medicine McGill University Montreal Quebec Canada Department of Genetics and Genome Sciences Institute for Systems Genomics UConn Health Farmington CT USA

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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Buparlisib and ponatinib inhibit aggressiveness of cholangiocarcinoma cells via suppression of IRS1-related pathway by targeting oxidative stress resistance

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Biomedicine and Pharmacotherapy 2024年 180卷 117569-117569页

作者： Kaewlert, Waleeporn Sakonsinsiri, Chadamas Lert-itthiporn, Worachart Mahalapbutr, Panupong Ali, Saba Rungrotmongkol, Thanyada Jusakul, Apinya Armartmuntree, Napat Pairojkul, Chawalit Feng, Guofei Ma, Ning Pinlaor, Somchai Murata, Mariko Thanan, Raynoo Department of Biochemistry Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Cholangiocarcinoma Research Institute Khon Kaen University Khon Kaen 40002 Thailand Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Program in Bioinformatics and Computational Biology Graduated School Chulalongkorn University Bangkok 10330 Thailand The Center for Research and Development of Medical Diagnostic Laboratories Faculty of Associated Medical Sciences Khon Kaen University Khon Kaen 40002 Thailand Department of Medical Science Amnatcharoen Campus Mahidol University Amnat Charoen 37000 Thailand Department of Pathology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand Department of Environmental and Molecular Medicine Mie University Graduate School of Medicine Mie 514-8507 Japan Graduate School of Health Science Suzuka University of Medical Science Suzuka Mie 510-0226 Japan Department of Parasitology Faculty of Medicine Khon Kaen University Khon Kaen 40002 Thailand

Cholangiocarcinoma (CCA) is an oxidative stress-driven liver cancer with bile duct epithelial cell phenotypes and currently lacks effective treatments, making targeted drug therapy urgently needed. Oxidative stress plays a critical role in CCA carcinogenesis, involving cells with oxidative stress resistance via upregulation of the PI3K and MEKK3 signaling pathways. In this study, we investigated the antineoplastic efficacy of a PI3K inhibitor (buparlisib) and a multi-tyrosine kinase inhibitor (ponatinib) on CCA. The cytotoxicity of the drug combination was studied in vitro using CCA cell lines and in vivo using CCA xenograft models. It was found that the drug combination suppressed growth, colony formation, and migration abilities of CCA cells and induced oxidative damage, cell cycle arrest, and autophagy by suppressing MEKK3 and YAP1 through inhibition of insulin receptor substrate 1 (IRS1) signaling. Moreover, the drugs would potentially bind to the IRS1 protein, significanly decreasing IRS1 phosphorylation. Additionally, the drug combination significantly diminished the expression of YAP1, the cell proliferation marker and an antioxidant regulator, and increased oxidative stress-responsive markers in the xenograft model. In conclusion, targeting oxidative stress resistance with combined buparlisib and ponatinib suppressed tumor growth and migration by repressing IRS1-related pathways and ultimately inducing oxidative damage, suggesting the potential for targeted therapy and clinical trials in CCA patients over the use of a single drug. © 2024 The Authors

关键词： Anti-cancer Bile duct cancer Buparlisib Insulin receptor substrate 1 Ponatinib Targeted therapy

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Erratum to: The real cost of sequencing: scaling computation to keep pace with data generation

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Genome biology 2016年第1期17卷 78页

作者： Paul Muir Shantao Li Shaoke Lou Daifeng Wang Daniel J Spakowicz Leonidas Salichos Jing Zhang George M Weinstock Farren Isaacs Joel Rozowsky Mark Gerstein Department of Molecular Cellular and Developmental Biology Yale University New Haven CT 06520 USA. Systems Biology Institute Yale University West Haven CT 06516 USA. Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. The Jackson Laboratory for Genomic Medicine Farmington CT 06032 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark@***. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark@***. Department of Computer Science Yale University New Haven CT 06520 USA. mark@***.

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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Ten quick tips for deep learning in biology

arXiv

引用

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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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Comprehensive Functional Characterization of Germline ETV6 Variants Associated with Inherited Predisposition to Acute Lymphoblastic Leukemia in Children

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BLOOD 2016年第22期128卷 1085-1085页

作者： Nishii, Rina Baskin, Rebekah Moriyama, Takaya Hoshitsuki, Keito Metzger, Monika L. Wu, Gang Devidas, Meenakshi Yang, Wenjian Tedrick, Paige Quinn, Emily Gastier-Foster, Julie M. Raetz, Elizabeth Larsen, Eric Martin, Paul L. Bowman, W. Paul Winick, Naomi Takagi, Masatoshi Pui, Ching-Hon Evans, William E. Zhang, Jinghui Relling, Mary V. Hunger, Stephen P. Mullighan, Charles Loh, Mignon L. Nichols, Kim E. Yang, Jun J. Hematological Malignancies Program and Department of Pharmaceutical Sciences St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Developmental Biology Tokyo Medical and Dental University Graduate School of Medicine Tokyo Japan Department of Oncology St. Jude Children's Research Hospital Memphis TN School of Pharmacy University of Pittsburgh Pittsburgh PA Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Biostatistics College of Medicine Public Health and Health Professions University of Florida Gainesville FL Department of Pathology and Laboratory Medicine Nationwide Children's Hospital Columbus OH Huntsman Cancer Institute University of Utah Salt Lake City UT Maine Children's Cancer Program Scarborough ME Department of Pediatrics Duke University Durham NC Cook Children's Medical Center Fort Worth TX Pediatric Hematology Oncology University of Texas Southwestern Medical Center Dallas TX Oncology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Pediatrics and Center for Childhood Cancer Research Children's Hospital of Philadelphia and University of Pennsylvania Perelman School of Medicine Philadelphia PA Department of Pathology St. Jude Children's Research Hospital Memphis TN Department of Pediatrics Benioff Children's Hospital and Helen Diller Family Comprehensive Cancer Center University of California San Francisco CA

1085

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