一种诊断和治疗疾病的数据驱动方法A Data Driven Approach to Diagnosing and Treating Disease |
|
课程网址: | http://videolectures.net/kdd2014_schadt_treating_disease/ |
主讲教师: | Eric Schadt |
开课单位: | 西奈山伊坎医学院 |
开课时间: | 2014-10-07 |
课程语种: | 英语 |
中文简介: | 在生物医学和生命科学研究界,先进的综合生物学算法被用于整合许多不同高维数据类型的大规模数据,以构建疾病的预测网络模型。我们为此目的使用的因果推理方法很好地补充了自然人工智能/机器学习方法的类型,这些方法在生命和生物医学科学中几乎成为标准,用于构建一系列问题的分类器,从疾病分类和亚型分层,涉及识别给定治疗策略的应答者和非应答者。通过建立一个跨越多个尺度(从分子到细胞,到组织/器官,再到生物体和社区)的因果网络模型,我们可以了解信息流,以及如何最好地调节信息流以改善人类福祉,无论是更好地诊断和治疗疾病还是改善整体健康(1-4)。更具体地说,我们构建了阿尔茨海默病以及其他常见人类疾病(如肥胖、糖尿病、心脏病、炎症性肠病和癌症)的预测网络模型,并证明了所有这些疾病的共同因果网络(3,5-10)。我们不仅证明了我们的预测模型揭示了疾病的重要机制和不同疾病之间的机制联系,而且它们为优先考虑干预的治疗点提供了一种自然的方法,并为高通量筛查提供了最佳的分子表型。我们将这些模型应用于许多疾病领域,从而确定了导致疾病的新基因,这些基因可以作为治疗干预的有效点,并制定了个性化的治疗策略,为针对特定形式的疾病定制治疗提供了更定量和准确的方法。 |
课程简介: | Throughout the biomedical and life sciences research community, advanced integrative biology algorithms are employed to integrate large scale data across many different high-dimensional datatypes to construct predictive network models of disease. The causal inference approaches we employ for this purpose well complement the types of natural artificial intelligence/machine learning approaches that have become nearly standard in the life and biomedical sciences for building classifiers for a range of problems, from disease classification and subtype stratification, to the identification of responders and non-responders for a given treatment strategy. By building a causal network model that spans multiple scales (from the molecular to the cellular, to the tissue/organ, to the organism and community) we can understand the flow of information and how best to modulate that flow to improve human wellbeing, whether better diagnosing and treating disease or improving overall health(1-4). More specifically, we have constructed predictive network models for Alzheimer's disease, along with other common human diseases such as obesity, diabetes, heart disease, and inflammatory bowel disease, and cancer, and demonstrated a causal network common across all of these diseases(3, 5-10). Not only do we demonstrate that our predictive models uncover important mechanisms of disease and mechanistic connections among different diseases, but that they have led to a natural way to prioritize therapeutic points of intervention and provide optimal molecular phenotypes for high throughput screening. Our application of these models in a number of disease areas has led to the identification of novel genes that are causal for disease and that may serve as efficacious points of therapeutic intervention, as well as to personalized treatment strategies that provide a more quantitative and accurate approach to tailoring treatments to specific forms of disease. |
关 键 词: | 诊断疾病; 治疗疾病; 数据驱动 |
课程来源: | 视频讲座网 |
数据采集: | 2023-06-11:chenxin01 |
最后编审: | 2023-06-11:chenxin01 |
阅读次数: | 28 |