• Fall 2019 Teaching:

    • CSCI 1020: Introduction to Computer Science: Bioinformatics
      Syllabus
    • Office Hours: Monday 10am-11:50am, Wednesday 10am-11:50am, Friday 10am-11:50am, or by appointment (email me: jie.hou@slu.edu)

About Me

I am an assistant professor in the Department of Computer Science at the Saint Louis University. I received my M.A. in Statistics in 2014, and Ph.D. in Computer Science in 2019 from University of Missouri-Columbia.
My research is mainly focused on developing data-driven computational methods (particularly machine learning, deep learning, and computational optimization methods) for protein structure and function prediction. In addition, I am interested in developing data mining methods to analyze omics (i.e. RNA-seq transcriptomics and genomics) data to study genes and gene networks.

Protein Structure Prediction

Protein Function Prediction

Omics data analysis

Softwares
   

Research

Data-assisted protein structure prediction

Data-restrained structural modeling based on experimental information has attracted new attention. Solutions to such problems are still under development. The use of experimental restraints, such as cross-linking/mass spectrometry, small-angle x-ray scattering (SAXS) and single-particle cryo-electron microscopy (cryo-EM) have emerged and are considered a promising direction in structural modeling improvement.
My research interest is focused on developing computational methods (i.e. deep learning, machine learning, optimization techniques) to determine the best ways to fully leverage the experimental information from SAXS/Cryo-EM in protein structure modeling.

Protein Function Prediction using Deep Learning

Publication

@Google Scholar

  1. J. Cheng, M. Choe, A. Elofsson, K. Han, J.Hou, A. Maghrabi, L.J. McGuffin, D. Menéndez-Hurtado, K. Olechnovič, T. Schwede, G. Studer, K. Uziela, Č. Venclovas, B. Wallner. Estimation of model accuracy in CASP13. Proteins, accepted.
  2. J. Hou, T. Wu, R. Cao, J. Cheng. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Proteins, accepted. (CASP13 invited paper).
  3. Hou, J., Adhikari, B., Tanner, J. J., & Cheng, J. (2019). SAXSDom: Modeling multi-domain protein structures using small-angle X-ray scattering data. bioRxiv, 559617.
  4. J. Hou, X. Shi, C. Chen, Md. Soliman, Adam F. Johnson, Tatsuo Kanno, Bruno Huettel, Ming-Ren Yen, Fei-Man Hsu, Tieming Ji, Paoyang Chen, Marjori Matzke, Antonius J.M. Matzke, Jianlin Cheng, James A. Birchler. Global impacts of chromosomal imbalance on gene expression in Arabidopsis and other taxa. Proceedings of the National Academy of Sciences (PNAS).2018.
  5. B. Adhikari, J. Hou, J. Cheng. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Proteins, 2018.
  6. Y. Bian, C. He, J. Hou, J. Cheng, J. Qiu. PairedFB: a full hierarchical Bayesian model for paired RNA-seq data with heterogeneous treatment effects. Bioinformatics, accepted. 2018
  7. Lei, W., Lu, Y., Hou, J., Chen, C., Browning, J. D., Lubahn, D. B., ... & Fritsche, K. L. (2018). RNA Sequence Analysis Reveals Expected and Novel Immuno-Modulatory Activities by Sutherlandia frutescens.
  8. J. Hou, B. Adhikari, J. Cheng. DeepSF: deep convolutional neural network for mapping protein sequences to folds. Bioinformatics 2017 Dec.
  9. B. Adhikari, J. Hou, J. Cheng. DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics 2017 Dec.
  10. J. Lingyan, Y. Wan, J. C. Anderson, J. Hou, S.M. Soliman, J. Cheng, S.C. Peck. Genetic Dissection of Arabidopsis MAP Kinase Phosphatase 1 (AtMKP1)-dependent PAMP-induced transcription pathways. Journal of Experimental Botany, 2017
  11. Jo T, Hou J, Eickholt J, Cheng J. Improving protein fold recognition by deep learning networks. Scientific reports. 2015 Dec 4;5:17573.
  12. Hou J, Stacey G, Cheng J. Exploring soybean metabolic pathways based on probabilistic graphical model and knowledge-based methods. EURASIP Journal on Bioinformatics and Systems Biology. 2015 Dec 1;2015(1):5.
  13. Hou J, Acharya L, Zhu D, Cheng J. An overview of bioinformatics methods for modeling biological pathways in yeast. Briefings in functional genomics. 2015 Oct 17:elv040.
  14. Li J, Hou J, Sun L, Wilkins JM, Lu Y, Niederhuth CE, Merideth BR, Mawhinney TP, Mossine VV, Greenlief CM, Walker JC. From gigabyte to kilobyte: a bioinformatics protocol for mining large RNA-Seq transcriptomics data. PloS one. 2015 Apr 22;10(4):e0125000.

Courses

An introduction to computer programming motivated by the analysis of biological data sets and the modeling of biological systems. Computing conceptsto include data representation, control structures, text processing, input andoutput. Applications to include the representation and analysis of proteinand genetic sequences, and the use of available biological data sets.

MWF 1:10pm-2:00pm

Contact

Ritter Hall 217