Integrating literature and experimental data for druggability methods

 描述（由申请人提供）： 精神分裂症是一种使人衰弱的精神障碍，影响到1%的普通人群，并导致无家可归和自杀的风险增加。由于精神分裂症的遗传成分还不清楚，患者的症状通常是治疗的重点。由于治疗药物种类有限，精神分裂症是药物再利用的主要候选者。在过去的十年里，即使有了参考基因组和丰富的实验数据，药物开发仍然停滞不前。由于高开发成本和在临床试验期间失败的可能性，药物发现集中在估计的3000个可药用人类基因上;然而，这种狭隘的关注严重限制了发现的潜力。估计有3000个可药用的人类基因，从各种来源，包括文献和基因表达数据，发现新药和现有药物的新用途的巨大潜力仍然存在。文献是已知信息的最全面的资源，更具体地说，是高质量低通量数据的最全面的资源。由于高通量实验数据的可用性，许多用于药物发现和再利用的计算方法都集中在将这些数据纳入其算法中;然而，从文献中提取的数据同样有价值。使用文献和 基于实验数据，我提出了以下具体目标：（1）从文献和实验数据源中整合药物、基因和疾病的关系;（2）利用蛋白质、药物和疾病与实验数据的相互作用网络来推断可药物化的蛋白质;（3）将网络模型应用于精神分裂症，以预测新的可药物化的蛋白质用于药物再利用。成功后，我将创建一种新的方法来整合文献和高通量实验数据，并应用这种方法来发现与精神分裂症相关的新型可药物化蛋白质，这些蛋白质可以为未来的药物再利用研究提供信息。

 DESCRIPTION (provided by applicant): Schizophrenia is a debilitating mental disorder that affects one percent of the general population and leads to an increased risk of homelessness and suicide. Because the genetic component underlying schizophrenia is not well understood, a patient's symptoms are usually the focus of treatment. With limited drug classes for treatment, schizophrenia is a prime candidate for drug repurposing. Over the last decade, drug development has remained stagnant even with the reference genome and an abundance of experimental data. Due to the high development cost and likelihood of failing during clinical trials, drug discovery has focused on an estimated 3000 druggable human genes; however, this narrow focus has severely limited the potential for discovery. With an estimated 3000 druggable human genes, great potential remains for discovery of new drugs and novel uses for current drugs from a variety of sources, including literature and gene expression data. Literature is the most comprehensive resource for known information and, more specifically, high quality low- throughput data. Due to the availability of high-throughput experimental data, many computational methods for drug discovery and repurposing have focused on incorporating this data into their algorithms; however, data extracted from the literature is equally valuable. To investigate druggability using literature and experimental data, I propose the following specific aims: (1) integrate drug, gene, and disease relationships from the literature and experimental data sources; (2) infer druggable proteins using networks of protein, drug, and disease interactions with experimental data; and (3) apply the network model to schizophrenia to predict novel druggable proteins for drug repurposing. With success, I will create a novel methodology to integrate literature and high-throughput experimental data and apply this methodology to discover novel druggable proteins related to schizophrenia that can inform future drug repurposing studies.