Dalkilic Mehmet M, Costello James C, Clark Wyatt T, Radivojac Predrag
School of Informatics, Indiana University, Bloomington, IN 47408, USA.
Front Biosci. 2008 May 1;13:3391-407. doi: 10.2741/2934.
Advancements in high-throughput technology and computational power have brought about significant progress in our understanding of cellular processes, including an increased appreciation of the intricacies of disease. The computational biology community has made strides in characterizing human disease and implementing algorithms that will be used in translational medicine. Despite this progress, most of the identified biomarkers and proposed methodologies have still not achieved the sensitivity and specificity to be effectively used, for example, in population screening against various diseases. Here we review the current progress in computational methodology developed to exploit major high-throughput experimental platforms towards improved understanding of disease, and argue that an integrated model for biomarker discovery, predictive medicine and treatment is likely to be data-driven and personalized. In such an approach, major data collection is yet to be done and comprehensive computational models are yet to be developed.
高通量技术和计算能力的进步使我们对细胞过程的理解取得了重大进展，包括对疾病复杂性有了更深入的认识。计算生物学界在表征人类疾病和实施将用于转化医学的算法方面取得了进展。尽管取得了这一进展，但大多数已识别的生物标志物和提出的方法仍未达到可有效用于例如针对各种疾病的人群筛查的敏感性和特异性。在此，我们回顾了为利用主要高通量实验平台以增进对疾病的理解而开发的计算方法的当前进展，并认为生物标志物发现、预测医学和治疗的综合模型可能是数据驱动且个性化的。在这种方法中，主要数据收集工作尚未完成，全面的计算模型也尚未开发出来。
