64-Bit High Performance Computing

描述(由申请者提供)：TGen科学家正在从事由NIH和其他来源资助的各种研究项目，这些项目开发和检查人类疾病的分子图谱。重点是识别对疾病诊断和预后有用的复杂或简单的生物标志物集，以及开发分子分类以指导最佳治疗选择和确定新的靶点。目前正在分析的分子图谱数据集包括恶性胶质瘤、黑色素瘤、胰腺癌、前列腺癌、结肠癌、多发性骨髓瘤、乳腺癌、阿尔茨海默氏症和自闭症。TGen科学家使用不同复杂程度的数学模型，从几个计算角度检查分子图谱，所有这些模型都试图识别在分子病理学中发挥关键作用的基因和基因网络。每一种观点都涉及基因-基因相互作用的某些方面，产生了一个组合问题，其中计算解受到计算机内存大小和处理能力的限制。TGen拥有一个由计算生物学家、生物信息学家和工程师组成的活跃小组，他们与TGen内外的生物医学和临床科学家密切合作，开发各种计算和统计工具，以解决复杂的生物医学问题。TGen开发的工具的例子包括：强大的特征选择算法；用于基因调控网络推理的网络增长算法；基于马尔可夫链的基因调控网络模拟；基于微阵列和临床数据的细胞背景选择；大型微阵列数据的聚集；分类和特征选择算法的稳健错误估计；SNP连锁和覆盖率分析；以及用于显著性分析的排列测试。正在分析的数据类型包括基因表达阵列、SNPs、CGH、siRNA和临床特征。其中许多工具需要相当大的计算能力和大量内存来检查解决方案空间的巨大复杂性。传统的32位计算体系结构不能寻址4 GB以上的内存。由于最优数学模型和计算算法所需的计算机分析时间长得令人望而却步，这种4 GB内存限制强加了不太理想的分析方法。位计算架构将允许开发计算模型和算法，这些计算模型和算法可以充分利用超过4 GB的内存空间。迄今为止，TGen科学家的成功是以牺牲时间为代价的。然而，受疾病影响的个人没有充裕的时间。所要求的位SMP计算器将优化TGen研究人员的能力，以高效地满足他们的数据分析需求，促进及时有效地发现生物标记物，改善人类健康。

DESCRIPTION (provided by applicant): TGen scientists are working on a variety of research projects funded through the NIH and other sources that develop and examine molecular profiles of human diseases. The focus is to discern complex or simple sets of biomarkers useful for disease diagnosis and prognosis, as well as to develop molecular classification for directing optimal therapeutic choice and identifying new targets. The molecular profile datasets currently being analyzed cover Malignant Gliomas, Melanoma, Pancreatic Cancer, Prostate Cancer, Colon Cancer, Multiple Myeloma, Breast Cancer, Alzheimer's, and Autism. TGen scientists examine molecular profiles from several computational perspectives, using mathematical models with varying degrees of complexity, all of which attempt to identify genes and gene networks that play crucial roles in the molecular pathology. Each of the perspectives involves some aspect of gene-gene interaction, creating a combinatorial problem where computational solutions are limited by the computer's memory size and processing power. TGen has an active group of computational biologists, bioinformaticians, and engineers who are closely working with biomedical and clinical scientists both within and outside TGen to develop various computational and statistical tools that address complex biomedical questions. Examples of the tools developed at TGen include the following: strong feature selection algorithm; network growth algorithm for gene regulatory network inference; Markov Chain-based simulation of gene regulatory networks; selection of cellular context based on microarray and clinical data; clustering of large microarray data; robust error estimation of classification and feature selection algorithms; SNP linkage and coverage analysis; and permutation tests for significance analysis. The data-types that are being analyzed include gene expression arrays, SNPs, CGH, siRNA, and clinical features. Many of these tools require considerable computing power and large amount of memory to examine the enormous complexity of the solution space. Conventional 32-Bit computing architecture cannot address memory above 4 GB. This 4 GB memory limitation imposes suboptimal analytical approaches due to the prohibitively protracted computer analysis time needed for optimal mathematical models and computational algorithms. A 64-Bit computing architecture will allow development of computational models and algorithms that can take a full advantage of memory space beyond 4 GB. The success of TGen scientists to date has come at the sacrifice of time. However, individuals affected with disease do not have the luxury of time. The requested 64-bit SMP computing instrument will optimize TGen researcher's ability to meet their data analysis needs efficiently, fostering timely and effective biomarker discovery for improved human health.