Limits of Inference from Biological Sequence Analysis

两个主要的发展是有趣的：生物学，在各种测量水平上产生大量的数据，关注有用的信息，无论是了解生物功能，并将任何这种理解转化为复杂疾病的治疗，是隐藏在这样的数据。机器学习-数学和计算科学的丰富组合-提供了工具，我们可以从大型复杂的数据集中提取有用的信息。大分子序列中保存着许多跨代遗传的生物学信息：指定调节分子可能结合和相互作用的短基序，免疫细胞中高度可变的受体序列，可以区分自我信号和入侵病原体的信号，以及群体水平序列中的基因座，可以为我们提供有关遗传性疾病变异的线索。在这个项目中，我们将研究基于深度学习的推理算法，用于从生物序列数据中提取有用的信息。我们将研究的一个特殊问题是学习表示-将序列数据映射到更方便的数学空间（连续和分布式）的艺术，其中模式识别方法的操作变得方便。我们将专注于这些模型的可解释性，以提取有关蛋白质相互作用，免疫反应和选择性剪接的特定信息。

Two major developments are of interest: Biology, generating vast amounts of data at various levels of measurements is concerned with how useful information, both to understand biological function and to translate any such understanding to the treatment of complex diseases, is hidden in such data. Machine learning -- a rich combination of mathematical and computational sciences -- provides tools with which we can extract useful information from large and complex datasets. Much information about biology, inherited across generations, is held in macromolecular sequences: short motifs specifying where regulatory molecules may bind and interact, highly variable receptor sequences in immune cells that can distinguish between signals of the self and invading pathogens and loci in population level sequences that can give us cues about variants responsible for inherited diseases. In this project we will study inference algorithms that are based in deep learning for extracting useful information from biological sequence data. A particular problem we will study is learning representations - the art of mapping sequence data onto more convenient mathematical spaces, continuous and distributed, in which their manipulation by pattern recognition methods becomes convenient. We will focus on interpretability of such models to extract specific information about protein interactions, immune response and alternative splicing.