School of EECS

Yichao Li

Ph.D student in Computer Science
Master student in Math
2014-2015 President of CLA
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Chinese Learner Association
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2015 Spring Festival Gala article 1
2015 Spring Festival Gala article 2
2015 Spring Festival Gala article 3

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Research Interest

  • Population Genetics

  • The field of population genetics are broad. I'm interested in studying the causes and consequences of genetic variants. It involves reconstructing the human genome, eQTL and QTL and its influence on gene regulation. The topics I am currently working on is:

    The First Draft of Native American Genome

    [Abstract]: The Native American has evolved for more than 15,000 years in the continent of America, yet no Native American Genome has been inferred. Here we take the advantage of existing human genetic variation data to reconstruction the Native American Genome. Our result has implications in both medical research and genetic ancestral test.

    The Allocation of Ancestral SNPs in Human Genome

    [Abstract]: A Single-nucleotide polymorphism (SNP) is a DNA sequence variation occurring at a single nucleotide position, for example, both G and A could occur at the same genomic position. According to coalescence theory, one SNP is the ancestral SNP and the others is derived SNP. Here we are using 1000 genome data, Neanderthal genome, Gorilla genome and inferred ancestral mammalian genome to allocate the ancestral SNPs in the human genome. Our result has implications in both medical research and genetic ancestral test.

    Genome geography based on Ancestral Informative SNPs

    [Abstract]: Ancestral Informative SNP (AISNP) is defined to guide the genetic ancestral test. More often, a set of AISNPs is unique to a particular population. Using this idea, we could divide genome into different segments such that each segment is unique to a particular race, and we name such genome as genome geography (GG). Note: both AISNP and GG are not my idea.
  • Regulatory genomics

  • Identification of response predictor

    Every cell line is regarded as a patient. Since each patient response differently to each drug, we will try to find the good response predictor using just a few genomic features. A response predictor is a set of features that could tell us whether this drug is a good treatment of this patient.

    Systematically identification of omic features associated with differential gene expression in breast cancer

    Breast cancer is a clinically and genomically heterogeneous disease. No single omic feature could explain the whole picture of the differential gene expression profile in breast cancer.
    - Comparing cell line-cell line with no stimulus
    - Comparing case-control within on cell line

    Systematical discovering target genes of Enhancers

    Systematical identification and annotation of lncRNA

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