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）：此共享设备拨款请求资金购买一台 256 个双四节点、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、实习生和工作人员使用的主要共享计算资源。该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。生物系统研究旨在利用实验和建模相结合的方法来了解心脏性猝死（SCD；西方世界死亡的主要原因）的机制。这项工作为 SCD 的分子和细胞机制提供了新的见解，并实现了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的最佳方法的“计算机模拟”设计。 ICM 计算解剖学 (CA) 研究旨在开发算法，用于发现大脑解剖结构和功能的变化，以及心脏结构/功能和运动的变化，从而预测大脑或心脏病的发展。通过分析获取的三维图像体积来测量正常群体和疾病群体的结构、功能和/或运动 来自使用磁共振成像或计算机断层扫描的患者。然后使用 CA 算法发现解剖结构、功能和/或运动的变化，从而高精度地区分正常群体和患病群体。这些方法用于早期诊断脑部或心脏病的发展，以便进行治疗干预。主要应用领域是发现疾病早期发作的特征，例如精神分裂症、阿尔茨海默氏型痴呆、缺血性与特发性扩张型心肌病以及 SCD 风险。数学生物信息学的研究旨在开发新颖的计算算法，对患者特异性的多尺度数据（例如，单核苷酸多态性数据、转录水平、蛋白质表达水平、成像数据和临床数据）进行操作，以发现生物标志物，从而准确、灵敏和具体地预测疾病的发作、阶段、风险和治疗方法。这些计算方法的应用非常广泛，包括癌症生物标志物的发现和 SCD 风险的早期预测，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非常重要的计算机模型和算法。 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。公众健康启示：所要求的仪器的获奖将极大地加速用于脑部疾病、心脏病和癌症的早期诊断和治疗的计算模型和算法的开发和应用。