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Research Areas

Much as there has been a revolution in the areas of biological and biomedical sciences through high-throughput data-generation technologies, a similar revolution has occurred in terms of the development in large-scale analysis approaches for computational biology and bioinformatics. Computational approaches to understanding protein structure, gene function, disease mechanism, drug development, imaging of biological systems, precision medicine, and the analysis of other large data sets (such as wearable device data and health records) are expected to contribute enormously to the analysis of biological systems and problems. The Yale BBS program offers an extraordinary opportunity for research in these exciting areas. Some representative areas are as follows.

Computational Genomics

A central problem in bioinformatics is the analysis of genomic information, culminating in the study of the human genome of an individual person (personal genomics). Research in genomic analyses includes annotating the genomes (such as coding and functional non-coding regions) through both computational and experimental approaches, dissecting gene regulation networks and signaling pathways, identifying disease-causing genes and variants (particularly related to neuroscience), and cancer genomics. Major developments in recent years are next-generation sequencing and single-cell technologies, which have driven the development of many computational approaches for analyzing and interpreting these data. Many track faculty are interested in the general problem of achieving an integrative and systems understanding of the whole genome. Yale, as a university, has a diverse set of activities in the areas of genomics and proteomics that are relevant to computational biology and bioinformatics.

Analysis of Macromolecular Structure

Fundamentally, the genome encodes the structures of molecules, the machines that carry out the work of the cell. Analyzing structures involves dealing with complex 3D shapes and simulating them based on physical principles. One of the grand challenges to computational biology is ab initio prediction of protein structures as well as the elucidation of structure-to-function relationships. Yale has a unique strength in both theoretical and empirical structural biology, including RNA and protein structure determination, protein packing, protein classification, and macromolecular motions.

Computational Immunology

Computational immunology (or systems immunology) involves the development and application of bioinformatics methods, mathematical models and statistical techniques for the study of immune system biology. The immune system is composed of dozens of different cell types and hundreds of intersecting molecular pathways and signals. Malfunctioning of this system has been linked to human diseases, such as diabetes, lupus, arthritis, allergies, and more. Systems approaches can be used to predict how the immune system will respond to a particular infection or vaccination, and to understand how to design an immunotherapy best. In addition, computational approaches are increasingly vital to understand the implications of the wealth of gene expression and epigenomics data being gathered from immune cells. Yale has a diverse research program in computational immunology that brings together expertise from a variety of scientific disciplines to bear on research projects in vaccine response, host-pathogen dynamics, cell-fate choices, immune genomics, informatics, and many other topics.

Heterogeneous Database Design & Knowledge Representation

All of bioinformatics deals with biological information. What is the best way of storing and organizing this on the computer? How does one best connect different types of information (e.g. protein features and expression data)? How can we interconnect distributed databases, handle terabytes of data, and provide biologically meaningful queries? Storing, managing, and accessing information is a nontrivial complex process that is one of the foundations of bioinformatics. Some CBB faculty develop creative informatics tools to analyze clinical and electronic health record data to inform health services research and implement these tools in informatics systems for clinical and research studies. Yale has long-standing strength in biomedical informatics as well as research in the development and interoperation of heterogeneous biomedical databases and related tools.

Developing Machine Learning Techniques & Efficient Algorithms

Both theoretical and practical biological problems generate unique algorithmic and computational problems, including machine learning, deep learning, combinatorial optimization, signal processing, and high-performance computing. For example, even simple processing of the extremely large-scale data generated by state-of-the-art genomics facilities requires considerable software and hardware development. Yale has experts in deep learning, algorithmic research, statistics, and advanced computing.