Learning to Predict Protein Structure with a Representation Reduction Approach

这个标准奖项是为了支持PostDoc与PI合作进行正在进行的研究，其目标是应用机器学习和信息理论的先进技术来研究蛋白质结构预测问题。 将使用的技术包括利用智能方式构建有用的复合属性，以及随着更好的方法得到认可，动态重新分配搜索工作。 这些技术不能盲目使用，而必须根据科学发现的特殊要求进行调整。 这项工作的结果应该对自动化和半自动化科学发现的许多方面具有广泛的适用性，但具体的焦点-理解蛋白质折叠-本身对蛋白质化学家和社会都非常重要，因为它将为研究和医学开辟新的机会。 此外，演示计算机辅助科学模型设计的一般技术应该有助于各种科学学科的科学家，因为他们记录的数据比他们可以有意义地手动分析的数据要多。

This standard award is for support of a PostDoc to work with the PI on his ongoing research whose objective is to apply advanced techniques from machine learning and information theory to study the problem of protein structure prediction. The techniques to be used include the utilization of intelligent ways of constructing useful composite attributes, and the dynamic reallocation of search effort as better approaches become recognized. Such techniques cannot be used blindly, but must be tailored to the special requirements of scientific discovery. The results of this work should have broad applicability to many aspects of automated and semi-automated scientific discovery, but the specific focus - understanding protein folding - is in itself extremely important to both protein chemists and society, because it will open new opportunities for research and medicine. Also, demonstrating a general technique for computer-aided scientific model design should help scientists in a variety of scientific disciplines, as they record more data than they can meaningfully analyze manually.