Computational systems biology  
line decor
line decor

Research Interests and Selected Publications

  1. Systems Genetics and Network Biology

    • Li H, Lu L, Manly KF, Chesler EJ, Bao L, Wang J, Zhou M, Williams RW and Cui Y (2005) Inferring Gene Transcriptional Modulatory Relations: A Genetical Genomics Approach. Human Molecular Genetics 14:1119-1125.

    • Li H, Chen H, Bao L, Manly KF, Chesler EJ, Lu L, Wang J, Zhou M, Williams RW and Cui Y (2006) Integrative Genetic Analysis of Transcription Modules: Towards Filling the Gap between Genetic Loci and Inherited Traits. Human Molecular Genetics 15:481-492.
    • Bao L, Wei L, Peirce JL, Homayouni R, Li H, Zhou M, Chen H, Lu L, Williams RW, Pfeffer LM, Goldowitz D and Cui Y. (2006) Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relations. Mammalian Genome 17:575-583.

    • Cui Y (2006) Elucidating gene regulatory networks underlying complex phenotypes: Genetical genomics and Bayesian network. In Shannon, F. (ed), Microarrays and Transcription Networks. Landes Bioscience, Page 114-126.

    • Bao L, Peirce JL, Zhou M, Li H, Goldowitz D, Williams RW, Lu L and Cui Y (2007) An integrative genomics strategy for systematic characterization of genetic loci modulating phenotypes. Human Molecular Genetics 16:1381-1390.

    • Cui Y (2007) Bridging Genotype and Phenotype: Causal Pathways from DNA to Complex Traits. In Deng, H.W. (ed), Current Topics in Human Genetics: Studies of Complex Diseases. World Scientific, Page 433-448.

    • Bao L, Xia X and Cui Y (2010) Expression QTL modules as functional components underlying higher-order phenotypes. PLoS One 5(12): e14313.

    • Ziebarth JD, Cook MN, Li B, Williams RW, Lu L and Cui Y (2010) Systems Genetics Analysis of Molecular Pathways Underlying Ethanol-Induced Behavioral Phenotypes. Biomedical Science and Engineering Conference - Biomedical Research and Analysis in Neuroscience (BRAiN). Oak Ridge, USA.  [May 25-26, 2010]

    • Ziebarth JD, Cook MN, Wang X, Williams RW, Lu L and Cui Y (2012) Treatment- and population-dependent activity patterns of behavioral and expression QTLs. PLoS One 7(2): e31805.

    • Emery FD, Parvathareddy J, Pandey AK, Cui Y, Williams RW and Miller MA (2014) Genetic control of weight loss during pneumonic Burkholderia pseudomallei infection. Pathogens and Disease, 71, 249-264.

    • Ha T, Swanson D, Larouche M, Glenn R, Weeden D, Zhang P, Hamre K, Langston M, Phillips C, Song M, Ouyang Z, Chesler E, Duvvurru S, Yordanova R, Cui Y, Campbell K, Ricker G, Phillips C, Homayouni R and Goldowitz D (2015) CbGRiTS: Cerebellar gene regulation in time and space. Developmental Biology 397, 18-30.

    • Bhattacharya A and Cui Y. (2017) A GPU-accelerated algorithm for biclustering analysis and detection of condition-dependent coexpression network modules. Scientific Reports 7: 4162.

  2. Molecular Disease Classification with Deep Learning Algorithms

    • Singh V, Baranwal N, Sevakula RK, Verma NK, and Cui Y (2016) Layerwise feature selection in Stacked Sparse Auto-Encoder for tumor type prediction. The IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Machine Learning and Big Data Research for Disease Classification and Complex Phenotyping Workshop. Shenzhen, China. [December 15-18, 2016]

    • Sevakula RK, Singh V, Kumar C, Verma NK and Cui Y. Transfer Learning for Molecular Cancer Classification using Deep Neural Networks. Under review.

  3. Disease Phenotype Prediction with Bayesian Network Modeling

    • Miyairi I, Ziebarth J, Laxton JD, Wang X, van Rooijen N, Williams RW, Lu L, Byrne G and Cui Y (2012) Host Genetics and Chlamydia Disease: Prediction and Validation of Disease Severity Mechanisms. PLoS One. 7(3): e33781.

    • Ziebarth JD, Bhattacharya A and Cui Y (2013) Bayesian Network Webserver: a comprehensive tool for biological network modeling. Bioinformatics 29: 2801-2803.

    • Cashion AK, Hathaway DK, Stanfill A, Thomas F, Ziebarth JD, Cui Y, Cowan PA, and Eason J (2014) Pre-transplant predictors of one year weight gain after kidney transplantation. Clinical Transplantation. 28, 1271-1278.

    • Ziebarth JD and Cui Y (2017) Precise Network Modeling of Systems Genetics Data using the Bayesian Network Webserver. In Schughart K and Williams RW (eds.), Systems Genetics: Methods and Protocols. Methods in Molecular Biology 1488:319-335.

  4. Functional Analysis of Genetic and Somatic Variants in microRNAs and Their Target Sites

    • Bao L, Zhou M, Wu L, Lu L, Goldowitz D, Williams RW and Cui Y (2007) PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits. Nucleic Acids Research 35: D51-D54.

    • Ziebarth JD, Bhattacharya A, Chen A and Cui Y (2012) PolymiRTS Database 2.0: Linking polymorphisms in microRNA target sites with human diseases and complex traits. Nucleic Acids Research 40: D216-D221.

    • Bhattacharya A, Ziebarth JD and Cui Y (2012) Systematic analysis of microRNA targeting impacted by small insertions and deletions in human genome. PLoS One 7(9): e46176.

    • Ziebarth JD, Bhattacharya A and Cui Y (2012) Integrative analysis of somatic mutations altering microRNA targeting in cancer genomes. PLoS One 7(10): e47137.

    • Bhattacharya A, Ziebarth JD and Cui Y (2013) SomamiR: A database for somatic mutations impacting microRNA function in cancer. Nucleic Acids Research 41:D977-D982.

    • Cui Y (2014) In silico mapping of polymorphic miRNA-mRNA interactions in autoimmune thyroid diseases. Autoimmunity 47, 327-333.

    • Bhattacharya A and Cui Y (2015) miR2GO: Comparative functional analysis for microRNAs. Bioinformatics 31, 2403-2405.  

    • Bhattacharya A and Cui (2015) Knowledge-based analysis of functional impacts of mutations in microRNA seed regions . Journal of Biosciences 40, 791-798.

    • Bhattacharya A and Cui Y (2016) SomamiR 2.0: a database of cancer somatic mutations altering microRNA-ceRNA interactions. Nucleic Acids Research 44, D1005-D1010.

    • Bhattacharya A and Cui Y (2017) Systematic prediction of the impacts of mutations in microRNA seed sequences. Journal of Integrative Bioinformatics 2017:20170001.

  5. Predicting Phenotypic Effects of coding SNVs with Machine Learning Algorithms

    • Bao L, Cui Y (2005) Prediction of the phenotypic effects of nonsynonymous single nucleotide polymorphisms. Bioinformatics 21:2185-2190.

    • Bao L, Zhou M, Cui Y (2005) nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Research 33: W480-W482.

    • Bao, L. and Cui, Y. (2006) Functional impacts of nonsynonymous single nucleotide polymorphisms: selective constraint and structural environments. FEBS Letters 580:1231-1234.

  6. Medical Image Analysis with Deep Learning and Clustering Algorithms

    • Verma NK, Gupta P, Agrawal P, Hanmandlu M, Vasikarla S and Cui Y (2009) Medical Image Segmentation Using Improved Mountain Clustering Approach. The 6th IEEE International Conference on Information Technology (ITNG) - New Generation, Las Vegas, Nevada, USA. [April 27-29, 2009]

    • Verma NK, Gupta P, Agrawal P and Cui Y (2009) MRI Brain Image Segmentation for Spotting Tumors Using Improved Mountain Clustering Approach, Applied Imagery Pattern Recognition (AIPR): Vision: Humans, Animals, and Machines. Washington DC, USA [October 14-16, 2009]

    • Singh V, Verma NK, Islam Z and Cui Y (2017) Feature Learning using Stacked Autoencoder for Shared and Multimodal Fusion of Medical Images. International Conference on Computational Intelligence: Theories, Applications and Future Directions. Indian Institute of Technology Kanpur, India. [December 6th - 8th, 2017]










Position available

Applications are invited for a postdoctoral fellow position in the computational systems biology lab at University of Tennessee Health Science Center. The successful candidate will be involved in developing deep learning algorithms for genomics research and precision medicine. The candidate must have obtained his/her Ph.D. degree in Bioinformatics, Computer Science or a related field. We are expecting high motivation and ability to work independently, as well as part of a research team. The candidate must have acquired a solid publication record and have excellent programming skills. Research experience in machine learning and computational biology is required. Preference will be given to candidates with knowledge of deep learning.

Salary will be commensurate with relevant experience. The position includes an excellent benefits package ( Located in Memphis, a dynamic Mid-South city rich in culture, history, diversity, music and cuisine, the University of Tennessee Health Science Center is one of the largest, most comprehensive academic health centers in the United States with a solid commitment to postdoctoral training.

HOW TO APPLY: Please send CV to Dr. Yan Cui (