Large-Scale Compute Cluster for the Institute for Computational Medicine

计算医学研究所的大规模计算集群

• 批准号: 7497781
• 负责人: RAIMOND Lester WINSLOW
• 金额: $ 200万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7497781
• 关键词: Algorithms; Alzheimer' s Disease; Anatomy; Area; Arrhythmia; Arts; Award; Bioinformatics; Biological; Biological Markers; Biological Models; Brain; Brain Diseases; Cause of Death; Clinical Data; Computational algorithm; Computer Simulation; Computers; Computing Methodologies; Data; Development; Disease; Early Diagnosis; Equipment; Experimental Models; Faculty; Funding; Goals; Grant; Heart; Heart Diseases; Image; Implantable Defibrillators; Institutes; Life; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Medicine; Memory; Methods; Molecular; Motion; Onset of illness; Patients; Placement; Population; Primary idiopathic dilated cardiomyopathy; Public Health; Research; Risk; Schizophrenia; Shock; Signal Transduction; Single Nucleotide Polymorphism; Staging; Structure; System; Therapeutic; Therapeutic Intervention; Three-Dimensional Image; Transcript; Universities; Western World; Work; X-Ray Computed Tomography; cluster computing; computing resources; design; early onset; heart motion; insight; instrument; novel; protein expression; sudden cardiac death

DESCRIPTION (provided by applicant): This shared equipment grant requests funds to acquire a 256 dual-quad node, large memory cluster computer. This computer will become the major shared computing resource used by faculty, trainees, and staff of the Institute for Computational Medicine (ICM) at Johns Hopkins University. This computer will support disease-related research in three major areas: a) modeling of biological systems; b) computational anatomy; and c) mathematical bioinformatics. Research in biological systems is directed at understanding the mechanisms of Sudden Cardiac Death (SCD; the major cause of death in the western world) using combined experimental and modeling approaches. This work is providing novel insights into the molecular and cellular mechanisms of SCD, and is enabling the "in-silico" design of optimal approaches for terminating life-threatening arrhythmias using shocks applied to the heart by implantable cardioverter defibrillators (ICDs). ICM research in computational anatomy (CA) is directed at developing algorithms for discovering changes in the anatomy and function of the brain, as well as changes in the structure/function and motion of the heart, that predictive developing brain or heart disease. Structure, function, and/or motion is measured in both normal and disease populations by analyzing three-dimensional image volumes acquired from patients using magnetic resonance imaging or computed tomography. CA algorithms are then used to discover changes in anatomical structure, function, and/or motion that distinguish normal and diseased populations with high accuracy. These methods are used for the early diagnosis of developing brain or heart disease so that therapeutic interventions can be made. Major application areas are the discovery of features that signal early onset of diseases such as schizophrenia, dementia of the Alzheimer's type, ischemic versus idiopathic dilated cardiomyopathy, and risk of SCD. Research in mathematical bioinformatics is directed at developing novel computational algorithms that operate on patient-specific multi-scale data (e.g., data on single nucleotide polymorphisms, transcript levels, protein expression levels, imaging data, and clinical data) to discover biomarkers for accurate, sensitive, and specific prediction of disease onset, stage, risk and therapeutic approach. Applications of these computational methods are broad, including discovery of cancer biomarkers and early prediction of risk for SCD so that ICD placement therapy may be performed. Achieving each of these goals requires the development and application of computer models and algorithms that are very computationally intensive. The present computing resources of the ICM are no longer state of the art, and research progress is being slowed. PUBLIC HEALTH REVELANCE: Award of the requested instrument will dramatically accelerate the development and application of computational models and algorithms for the early diagnosis and treatment of brain disease, heart disease, and cancer.

描述（由申请人提供）：这项共享设备赠款要求资金购买256双四节点，大内存集群计算机。这台计算机将成为约翰霍普金斯大学计算医学研究所（ICM）的教师、学员和工作人员使用的主要共享计算资源。这台计算机将支持三个主要领域的疾病相关研究：a）生物系统建模; B）计算解剖学; c）数学生物信息学。生物系统的研究旨在通过实验和建模相结合的方法来了解心脏性猝死（SCD;西方世界的主要死因）的机制。这项工作为SCD的分子和细胞机制提供了新的见解，并使“计算机”设计的最佳方法能够通过植入式心律转复除颤器（ICD）对心脏施加电击来终止危及生命的心律失常。计算解剖学（CA）中的ICM研究旨在开发用于发现大脑解剖结构和功能变化以及心脏结构/功能和运动变化的算法，这些变化可以预测大脑或心脏疾病的发展。通过分析采集的三维图像体积，测量正常和疾病人群的结构、功能和/或运动 使用磁共振成像或计算机断层扫描的患者。CA算法然后用于发现解剖结构、功能和/或运动的变化，其以高精度区分正常和患病群体。这些方法用于早期诊断发展中的大脑或心脏疾病，以便进行治疗干预。主要的应用领域是发现疾病早期发作的信号特征，如精神分裂症、阿尔茨海默型痴呆、缺血性与特发性扩张型心肌病以及SCD的风险。数学生物信息学的研究旨在开发对患者特异性多尺度数据（例如，关于单核苷酸多态性、转录水平、蛋白质表达水平、成像数据和临床数据的数据）来发现用于准确、灵敏和特异性预测疾病发作、阶段、风险和治疗方法的生物标志物。这些计算方法的应用是广泛的，包括发现癌症生物标志物和早期预测SCD的风险，以便可以进行ICD放置治疗。实现这些目标中的每一个都需要开发和应用计算机模型和算法， 计算密集型。ICM目前的计算资源不再是最先进的，研究进展正在放缓。公共卫生部门：所要求的仪器的授予将大大加快计算模型和算法的开发和应用，用于脑疾病，心脏病和癌症的早期诊断和治疗。