Faculty Profile

Li Liao

Li Liao

Associate Professor
424 Smith Hall
Newark, Delaware 19716
P: 302-831-3500
liliao@udel.edu

Personal Website



EDUCATION

PhD | 1992 | Peking University

SHORT BIO

My research interests are in bioinformatics. I am particularly interested in developing statistical and machine learning methods that are effective, expressive and interpretable, via incorporating domain specific knowledge of biological systems.

RESEARCH AREAS

  • Biomedical Informatics/Computational Biomedicine
  • Machine Learning

SELECT PUBLICATIONS

  1. Pakeeza Akram and Li Liao. Predicting Comorbid Diseases with Geometric Embedding of Human Interactome. The 14th International Symposium on Bioinformatics Research and Applications (ISBRA), Beijing, China, June 8 – 11, 2018
  2. Lei Huang, Li Liao, and Cathy Wu. Completing sparse and disconnected protein-protein networks by deep learning. BMC Bioinformatics 19:103, 2018.
  3. Pakeeza Akram and Li Liao. Prediction of missing common genes for disease pairs using network based module separation on incomplete human interactome, BMC Genomics 2017 18(Suppl 10):902.
  4. Lei Huang, Li Liao, and Cathy Wu. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space, PLoS ONE 12(9): e0183495.
  5. Pakeeza Akram and Li Liao. Cancer Specific Non-Synonymous Single Nucleotide Polymorphism Prediction in the Context of Haplotype and Protein Interacting Sites. Journal of Biomedical Science and Engineering, 2017:10, 28-44. doi: 10.4236/jbise.2017.105B004.
  6. Tianchuan Du, Li Liao, and Cathy H. Wu. Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.EURASIP Journal on Bioinformatics and Systems Biology 2016:17
  7. Colin Kern and Li Liao. A Post-Decoding Re-Ranking Algorithm for Predicting Interacting Residues in Proteins Computational Biology and Chemistry, 2016
  8. Pakeeza Akram and Li Liao. Prediction of missing common genes for disease pairs using network based module separation. The 6th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) October 13-15, 2016, Atlanta, GA, USA
  9. Lei Huang, Li Liao, and Cathy Wu. Protein-protein interaction prediction based on multiple kernels and partial network with linear programming. BMC Systems Biology, 2016 10(Suppl 2):45.
  10. Tianchuan Du, Li Liao, Cathy Wu, Bilin Sun. Residue-residue contact matrix prediction with Fisher score features and deep learning. Methods, S1046-2023(16)30156-6.
  11. Lei Huang, Li Liao, and Cathy H. Wu. Inference of protein-protein interaction networks from multiple heterogeneous data. EURASIP Journal on Bioinformatics and Systems Biology, 2016:8.
  12. Lei Huang, Li Liao, and Cathy Wu. Protein-protein interaction network inference from multiple kernels with optimization based on random walk by linear programming. IEEE International Conference on Bioinformatics & Biomedicine (BIBM2015), pp. 201 – 207, Washington, DC, USA, November 9-12, 2015.
  13. Tianchuan Du and Li Liao. Deep Neural Networks with Parallel Autoencoders for Learning Pairwise Relations: Handwritten Digits Subtraction. The 14th IEEE International Conference on Machine Learning and Applications. pp. 575 – 580, Miami, Florida, USA, December 9-11, 2015.
  14. Lei Huang, Li Liao, and Cathy H. Wu. Inference of protein-protein interaction networks from multiple heterogeneous data. The 2nd International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2015). Atlanta, Georgia, USA, September 9, 2015.
  15. L. Huang, L. Liao, and C.H. Wu. Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics , 2014. 10.1109/TCBB.2014.2366748
  16. Colin Kern and Li Liao. Lattice Models with Asymmetric Propensity Matrices for Locationally Informed Protein Structure Prediction. IEEE International Conference on Bioinformatics & Biomedicine (BIBM2013), pp. 90-93, Shanghai, China, December18-21, 2013.
  17. L. Liao, “Data Fusion with Optimized Block Kernels in LS-SVM for Protein Classification,” Engineering, Vol. 5 No. 10B, 2013, pp. 223-236. doi: 10.4236/eng.2013.510B048.
  18. Tianchuan Du, Li Liao, and Cathy Wu. Prediction of Protein-Protein Interaction Sites at Interface Topology Level. Proceedings of The International Conference on Bioinformatics and Computational Biology (BioComp13). pp.231-237, Las Vegas, Nevada, USA, July 22-25, 2013.
  19. Colin Kern, Alvaro J. Gonzalez, Li Liao and Vijay Shanker. Predicting Interacting Residues Using Long-Distance Information and Novel Decoding in Hidden Markov Models. IEEE Transactions on NanoBioscience, 2013, 12: 158-164.
  20. Alvaro Gonzalez, Li Liao and Cathy Wu. Prediction of contact matrix for protein-protein interaction. Bioinformatics, 2013, 29:1018-1025.
  21. Colin Kern, Alvaro Gonzalez, Li Liao, and Vijay Shanker. Improving Interacting Residue Prediction Using Long Distance Information in Hidden Markov Models. Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM12). pp. 179-184, Philadelphia, PA, October 4-7, 2012
  22. Alvaro J. Gonzalez, Li Liao and Cathy H. Wu. Predicting ligand binding residues and functional sites using multi-positional correlations with graph theoretic clustering and kernel CCA. IEEE/ACM Transaction on Computational Biology and Bioinformatics, 9(4):992-1001, 2012
  23. Kevin McCormick and Li Liao. Prediction and Evaluation of miRNA – target Gene Pairs Using K-means Clustering and Bipartite Graphs with Statistical Scoring. Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM11). pp.273-277, Atlanta, Georgia, November, 2011.
  24. Garret Molholt and Li Liao. A hybrid approach to assessing spoken fluency combining three metrics with support vector machines. Proceedings of Biomedical Engineering and Informatics (BMEI). pp. 2228-2231, Shanghai, China, October 15-17, 2011.
  25. Colin Kern, Li Liao, and K. Vijay-Shanker. Improving transmembrane protein prediction by increasing the utilization of local information. Proceedings of The 3rd International Conference on Bioinformatics and Computational Biology (BICoB). pp. 1-6, New Orleans, March, 2011.
  26. Cathy H. Wu, Jerry W. McLarty, and Li Liao. Neural networks. ENCYCLOPEDIA OF LIFE SCIENCES. John Wiley & Sons, 2010.
  27. Alvaro Gonzalez, Li Liao, and Cathy Wu. Predicting ligand binding residues using multi-positional correlations and kernel canonical correlation analysis. Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (BIBM10). pp. 158-163, Hong Kong, 18-21 Dec 2010.
  28. Alvaro J. Gonzalez and Li Liao. Predicting Domain-Domain Interaction Based on Domain Profiles with Feature Selection and Support Vector Machines. BMC Bioinformatics, 2010, 11:537.
  29. A. J. Gonzalez, Li Liao and Cathy H. Wu, Predicting Functional Sites in Biological Sequences Using Canonical Correlation Analysis, Proceedings of The 2010 International Conference on Bioinformatics & Computational Biology, pp.269-273, Las Vegas, Nevada, July 2010.
  30. Roger A. Craig, Jin Lu, Jinquan Luo, Lei Shi, and Li Liao. Optimizing nucleotide sequence ensembles for combinatorial protein libraries using a genetic algorithm. Nucleic Acids Research 38(2):e10 2010.
  31. A. J. Gonzalez, Li Liao, Constrained Fisher Scores Derived From Interaction Profile Hidden Markov Models Improve Protein-Protein Interaction Prediction, 1st International Conference on Bioinformatics and Computational Biology, pp. 236-247, New Orleans, Louisiana, April 2009.
  32. Gaurav Jain, Haozhu Wang, Li Liao, and E. Fidelma Boyd. Genomic Comparison of Bacterial Species Based on Metabolic Characteristics. Proceedings of The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)}. pp. 77-83, Shanghai, China, August, 2009.
  33. Li Liao. Inferring Protein Functional Linkage Based on Sequence Information and Beyond. Biological Data Mining, pp. 353-376, Editors: Jake Chen, Stefano Lonardi, & Randi Cohen, Chapman & Hall/CRC Press, 2009. ISBN-10: 1420086847, ISBN-13: 978-1420086843
  34. Li Liao. Probabilistic models for long-range features in biosequences. Machine Learning in Bioinformatics, pp. 241-261, Editors: Yanqing Zhang & Jagath C Rajapakse, Wiley, 2008. ISBN: 978-0-470-11662-3
  35. Roger A. Craig, Keyur Malaviya, Krishna Balasubramanian and Li Liao. Inferring functional linkage from residue level co-evolution information. The proceedings of The International Conference on Bioinformatics and Computational Biology (BioComp08), pp. 75-80, Las Vegas, Nevada, USA, July, 2008.
  36. Kevin McCormick, Roli Shrivastava and Li Liao. SlopeMiner: An improved method for mining subtle signals in time course microarray data. The International Frontiers of Algorithmics Workshop (FAW08). Changsha, China, June, 2008. LNCS 5059, pp. 28-34, Springer-Verlag Berlin Heidelberg 2008.
  37. Alvaro Gonzalez and Li Liao, Clustering Exact Matches of Pairwise Sequence Alignments by Weighted Linear Regression. BMC Bioinformatics 2008, 9:102.
  38. Tapan Patel and Li Liao, Predicting protein-protein interaction using Fisher scores extracted from domain profiles. Proceedings of IEEE 7th International Symposium for Bioinformatics and Bioengineering (BIBE), October 2007, Boston, MA.
  39. Li Liao and Zhongwei Li, Correlation Between Gene Silencing Activity and Structural Features of Antisense Oligodeoxynucleotides and Target RNA. In Silico Biology, 7:0036, 2007.
  40. Roger A. Craig and Li Liao, Improving Protein-Protein Interaction Prediction based on Phylogenetic Information using Least-Squares SVM. Annals of The New York Academy of Sciences, Vol. 1115, No. 1, pp. 154-167, 2007.
  41. Roger Craig and Li Liao, Phylogenetic Tree Information Aids Supervised Learning for Predicting Protein-Protein Interaction Based on Distance Matrices. BMC Bioinformatics 2007, 8:6.
  42. Roger Craig and Li Liao, “Transductive Learning with EM Algorithm to Classify Proteins Based on Phylogenetic Profiles”, Int. J. Data Mining and Bioinformatics 2007, 1:337-351.
  43. Roger Craig and Li Liao, “Prediction of Antisense Oligonucleotide Efficacy Using Local and Global Structure Information With Support Vector Machines”, The Proceedings of the fifth International Conference on Machine Learning and Applications (ICMLA’06), pp. 119-204, December 2006, Orlando, Florida.
  44. Tapan Patel, Manoj Pillay, Rahul Jawa, and Li Liao, “Information of Binding Sites Improves Prediction of Protein-Protein Interaction”, The Proceedings of the fifth International Conference on Machine Learning and Applications (ICMLA’06), pp. 205-210, December 2006, Orlando, Florida.
  45. Roger Craig and Li Liao, “Protein Classification Using Transductive Learning on Phylogenetic Profiles”, The Proceedings of The 21st Annual ACM Symposium on Applied Computing: Bioinformatics Track. pp. 161-166, April, 2006, Dijon, France.
  46. Li Liao, “Hierarchical profiling, scoring and applications in bioinformatics”, Advanced Data Mining Technologies in Bioinformatics, edited by Hui-Huang Hsu, Idea Group, Inc., 2006.
  47. Roger Craig and Li Liao, “Iterative Weighting of Phylogenetic Profiles Increases Classification Accuracy”, The Proceedings of The Fourth International Conference on Machine Learning and Applications (ICMLA’05), pp. 156-161, December, 2005, Los Angeles, California.
  48. Robel Kahsay, Guang Gao, and Li Liao, “Discriminating Transmembrane Proteins From Signal Peptides Using SVM-Fisher Approach”, The Proceedings of The Fourth International Conference on Machine Learning and Applications (ICMLA’05), pp. 151-155, December, 2005, Los Angeles, California.
  49. Robel Kahsay, Guoli Wang, Guang Gao, Li Liao and Roland Dunbrack, “Quasi-consensus based comparison of profile hidden Markov models for protein sequences”, Bioinformatics, vol. 21, pp. 2287-2293, 2005. (Recommended by Faculty 1000 Biology)
  50. Robel Kahsay, Guang Gao, and Li Liao, “An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes”, Bioinformatics, vol. 21, pp. 1853-1858, 2005.
  51. Kishore Narra and Li Liao, “Use of extended phylogenetic profiles with E-values and support vector machines for protein family classification”, The International Journal of Computer and Information Sciences, Vol. 6, No. 1, pp. 58-63, 2005.
  52. Robel Kahsay, Li Liao, and Guang Gao, “An improved hidden Markov model for transmembrane topology prediction”, The Proceedings of The 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 634-639, November 2004, Boca Raton, Florida.
  53. Kishore Narra and Li Liao, “Using extended phylogenetic profiles and support vector machines for protein family classification”, The Proceedings of the Fifth International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD04), pp. 152-157, Beijing, China.
  54. Li Liao and William Stafford Noble, “Combining Pairwise Sequence Similiarity and Support Vector Machines for Detecting Remote Protein Evolutionary and Structural Relationships”, Journal of Computational Biology, 10(2003)857-868.
  55. Li Liao, Sun Kim, and Jean-Francois Tomb, “Genome Comparisons Based on Profiles of Metabolic Pathways”, The Proceedings of The Sixth International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2002), pp469-476, September 2002, Crema, Italy.
  56. Sen Zhang, Li Liao, Jean-Francois Tomb, Jason T. L. Wang, “Clustering and Classifying Enzymes in Metabolic Pathways: Some Preliminary Results”, ACM SIGKDD Workshop on Data Mining in Bioinformatics (BioKDD2002), pp19-24, Edmonton, Canada, July 2002.
  57. Li Liao, William Stafford Noble, “Combining pairwise sequence similarity and support vector machines for remote protein homology detection”, The Proceedings of The Sixth International Conference on Research in Computational Molecular Biology (RECOMB 2002), April 2002, pp225-232. (http://www.erpland.org/recomb/papers.html).
  58. Derek W. Wood et al, “The Genome of Agrobacterium tumefaciens C58: Insights into the evolution and biology of a natural genetic engineer”, Science 294(2001)2317-2323.
  59. Sun Kim, Li Liao, Jean-Francois Tomb, “A Probabilistic Approach to Sequence Assembly Validation”, ACM SIGKDD Workshop on Data Mining in Bioinformatics (BioKDD2001), San Francisco, California, August 2001, pp38-43.
  60. Rick W. Ye, Wang Tao, Laura Bedzyk, Thomas Young, Mario Chen, and Li Liao, “Global Gene Expression Profiles of Bacillus subtilis Grown under Anaerobic Conditions”, Journal of Bacteriology, 182(2000)4458.
  61. Sun Kim, Li Liao, Michael Perry, Shiping Zhang, and Jean-Francois Tomb, “A Computational Approach to Sequence Assembly Validation”, Poster The 8th International Conference on Intelligent System for Molecular Biology, San Diego, California, August, 2000.
  62. Li Liao, Sun Kim, and Jean-Francois Tomb, “Clustering Protein Sequences using Linkage Graph”, Poster, The 8th International Conference on Intelligent System for Molecular Biology, San Diego, California, August, 2000.
  63. P. Zhang, X. Ye, L. Liao, J. Russo and S. Fischer, “Integrated Mapping Package–A Physical Mapping Software Tool Kit”, Genomics 55(1999)78.
  64. Liu Y., X.Q. Li, L. Liao, and J.F., Lu, “q-deformed SU_q(2), quark mixing and the Cabibbo-Kabayashi-Maskawa matrix”, Commun Theor. Phys., 27(1997)469.
  65. Li Liao, “Canonical Transformations of q-Bosonic Oscillators”, Commun Theor. Phys., 24(1995)121.
  66. Yibing Din, Li Liao, and Deming Ren, “Path to Unification, Research on Several Frontier Topics of Theoretical Physics in The 1990’s”, ISBN 7-5357-2129-X, Hunan Science and Technology Press, 1995.
  67. Li Liao & Xing-Chang Song, “On q-Differential Representation of Quantum Lie Superalgebras”, ICTP Preprint, IC/94/7, Trieste, Italy.
  68. Xing-Chang Song & Li Liao, “More about the q-deformed H-atom wave functions”, ICTP Preprint, IC/94/8, Trieste, Italy.
  69. Li Liao & Xing-Chang Song, “A Real Structure of q-Euclidean Space”, Int. J. Mod. Phys A (Proc. Suppl.) 3A(1993)471.
  70. Li Liao & Xing-Chang Song, “A Real Structure of q-Euclidean Space and Differential Calculus”, Science in China, A23(1993)606.
  71. Xing-Chang Song & Li Liao, “Canonical Differential Calculus on Quantum Orthogonal Groups”, Preprint CCAST-93-02, 1993, Beijing.
  72. Li Liao & Xing-Chang Song, “Spinor and Oscillator Representations of Quantum Enveloping Algebras of Type B_n, C_n and D_n”, Commun. Theor. Phys. 17(1992)439.
  73. Xing-Chang Song & Li Liao, “New Type Covariant Differential Calculus On the Quantum Planes”, Commun. Theor. Phys. 17(1992)323.
  74. Xing-Chang Song & Li Liao, “Quantum Schrodinger Equation and q-Deformation of Hydrogen Atom” , Journal of Physics A25(1992)623-634.
  75. Li Liao & Xing-Chang Song, “Quantum Lie Superalgebras and Non-Standard Braid Group Representations”, Modern Physics Letters A6(1991)959-968.
  76. Li Liao & Xing-Chang Song, “q-Oscillators and Quantum Lie Superalgebras”, Commun. Theor. Phys. 16(1991)249-256.
  77. Li Liao & Xing-Chang Song, “q-Deformation of Lie Superalgebra B(m,n), B(0,n), C(1+n) and D(m,n) in Their Boson-Fermion Representation”, Journal of Physics, A24(1991)5451-5463.
  78. Xue-Qian Li, Q.R. Wen, and Li Liao, “A phenomenological approach to Omega- radiative decay”. Commun. Theor. Phys. 13(1990)91-98.
  79. Li Liao & Xing-Chang Song, “Irreducible Representation of Quantum sl(3) Enveloping Algebra”, Commun. Theor. Phys. 13(1990)209-216.
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