Lee Sael

Current Research

My research focuses on machine learning for healthcare, tensor analysis/data mining, and bioinformatics.

Machine learning for healthcare
- INTERPRETABLE machine learning
- Deep learning for healthcare
- Multiple kernel learning for INTEGRATIVE analysis
Tensor analysis/data mining
- Compact and INTERPRETABLE tensor decomposition
- Heterogeneous data INTEGRATION
- Graph analysis
Bioinformatics
- Causal gene prioritization via prior knowledge INCORPORATION
- Target detection & Adverse drug effects prediction
- 3D structure search

AWARDS

Best Paper Award (runner up), 6th International Conference on Emerging Databases (EDB), Oct. 19, 2016
Best Poster Award, International Conference on Structure Genomics 2011, May 14, 2011
Best Paper Award, The 21th International Conference on Genome Information (GIW). Dec. 18, 2010

Current Grants

[2018.09-2021.08] PI: Basic Science Research Program, "Interpretable and Dynamic Tensor Decomposition Method Development and Applications to Personalized Clinical Analysis (NRF-2018R1A1A3A0407953)," supported by the National Research Foundation of Korea (NRF).
[2018.08-2025.02] Co-PI: Engineering Research Center (ERC), "Extreme Exploitation of Dark Data (NRF-2018R1A5A1060031)," supported by the National Research Foundation of Korea (NRF).
other Ajou University Grants and Group Grants (BK, Software etc)

* Completed Grant Projects

Talks

Tutorials

Sael Lee (Dec. 21, 2018), Interpretable Machine Learning at Korea Software Congress (KSC) 2018.
Sael Lee (Feb. 16, 2017), Mining and Learning Bio-Big Data at BIML2017.
Sael Lee (Jun. 18-20, 2017), Deep Learning in BioHealth at Korea Computer Congress (KCC) 2017.

Invited Talks

Sael Lee (Nov. 15, 2017) "Making Tensor Factorization more Interpretable." at Korea AI Society Workshop jointly held with ACML 2017.
Sael Lee (Nov. 2, 2017) "Scalable Tensor Factorization and Applications in Bioinformatics" at GIW/BIOINFO 2017.

Publications (selected)

Sael, L.* denotes that Sael Lee is the corresponding or the 1st author.

2021

Part, M., Jang, JG & Sael, L.* (2021). VEST: Very Sparse Tucker Factorization of Large-Scale Tensors BigComp2021.
[PDF]

2020

Jung, J. & Sael, L.* (2020). Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach. Machine Learning 109: 2333–2347.
[Link]
Lee, K, Hong, G., Sael, L., Lee, S., & Kim, HY. (2020). MultiDefectNet: Multi-Class Defect Detection of Building Façade Based on Deep Convolutional Neural Network. Sustainability 12 (22), 9785.
[PDF]
Choi, YJ, Ohn, JH, Sael, L. et al. (2020). Family-based exome sequencing combined with linkage analyses identifies rare susceptibility variants of MUC4 for gastric cancer. PloS ONE 15 (7), e0236197.
[PDF]

2019

Yoo, J. & Sael, L.* (2019). EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees. IEEE International Conference on Data Mining (ICDM)
[PDF] [BIBTEX] [GITHUB]
Lim, Y., Yu, I., Seo, D., Kang, U & Sael, L.* (2019). PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks. BMC Bioinformatics 20(S13), article 381
[PDF] [BIBTEX] [GITHUB]
Oh, S., Park, N., Jang, J., Sael, L. & Kang, U (2019). High-Performance Tucker Factorization on Heterogeneous Platforms. IEEE Transactions on Parallel and Distributed Systems (TPDS) 30(10), 2237-2248
Choi, D., & Sael, L.* (2019). SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) preprint.
[PDF] [BIBTEX] [GITHUB]

2018

Lee, J., Oh, S., & Sael, L.* (2018). GIFT: Guided and Interpretable Factorization for Tensors with an Application to Large-Scale Multi-platform Cancer Analysis. Bioinformatics, 34(24), 4151–4158.
[PDF] [BIBTEX] [GITHUB]
Lee, J., Choi, D., & Sael, L.* (2018). CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions. PLOS One 13(7):e0200579.
[PDF] [BIBTEX] [GITHUB]
Dehiya, V., Thomas, J., & Sael, L.* (2018). Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease. PLOS One 13(9):e0204101.
[PDF] [BIBTEX]
Oh S., Park, N., Sael, L., & Kang, U. (2018). Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries. 34th IEEE International Conference on Data Engineering (ICDE) 2018, Paris, France.
[PDF] [BIBTEX] [SUP]

2017

Thomas, J., & Sael, L.* (2017). Multi-Kernel LS-SVM Based Integration Bio-Clinical Data Analysis and Application to Ovarian Cancer. International Journal of Data Mining and Bioinformatics. IJDMB, 19:(2), 150-167.
[PDF] [BIBTEX]
Thomas, J., Thomas, S., & Sael, L.* (2017). DP-miRNA: an improved prediction of precursor microRNA using deep learning mode. In IEEE International Conference on Big Data and Smart Computing (BigComp)
[PDF] [BIBTEX]
Shin, K., Sael, L., & Kang, U. (2017). Fully scalable methods for distributed tensor factorization. IEEE Transactions on Knowledge and Data Engineering (TKDE), 29(1), 100-113.
[PDF] [BIBTEX]
Jung, J., Park, N., Sael, L., & Kang, U. (2017). BePI: Fast and memory-efficient method for billion-scale random walk with restart. In ACM International Conference on Management of Data (SIGMOD). Raleigh, North Carolina, USA.: ACM Press.
[PDF] [BIBTEX]

2016

Thomas, J., & Sael, L.* (2016). Maximizing information through multiple kernel-based heterogeneous data integration and applications to ovarian cancer. In 6th International Conference on Emerging Databases (EDB). ACM Press.
Best Paper Award (runner up)
[PDF] [BIBTEX]
Thomas, J., Seo, D., & Sael, L.* (2016). Review on graph clustering and subgraph similarity based analysis of neurological disorders. International Journal of Molecular Sciences, 17(6), 862.
[PDF] [BIBTEX]
Jeon, B., Jeon, I., Sael, L., & Kang, U. (2016). SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries. In 32nd IEEE International Conference on Data Engineering (ICDE). Helsinki, Finland.
[PDF] [BIBTEX]
Jeon, I., Papalexakis, E. E., Faloutsos, C., Sael, L., & Kang, U. (2016). Mining billion-scale tensors: algorithms and discoveries. The VLDB Journal.
[PDF] [BIBTEX]

2015

Kim, S., Sael, L.*, & Yu, H.* (2015). A mutation profile for top- k patient search exploiting Gene-Ontology and orthogonal non-negative matrix factorization. Bioinformatics, 31(22): 3653-3659.
[PDF] [BIBTEX] [PUBMED]
Sael, L.*, Jeon, I., & Kang, U. (2015). Scalable tensor mining. Big Data Research, 2(2), 82-86.
[PDF] [BIBTEX]
Thomas, J., & Sael, L.* (2015). Overview of integrative analysis methods for heterogeneous data. In The 2015 International Conference on Big Data and Smart Computing (BigComp) (pp. 266-270).
[PDF] [BIBTEX]
Kim, S., Sael, L., & Yu, H. (2015). LMDS-based approach for efficient top-k local ligand-binding site search. International Journal of Data Mining and Bioinformatics, 12(4), 417-433.
[PDF] [BIBTEX]

2014

Kang, D., Lim, W., Shin, K., Sael, L., & Kang, U. (2014). Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression. In Proceedings of the 23rd International Conference on Information and Knowledge Management (CIKM). Shanghai, China: ACM Press.
[PDF] [Supplementary Document] [BIBTEX]
Xiong, Y., Esquivel-Rodriguez, J., Sael, L., & Kihara, D. (2014). 3D-SURFER 2.0: Web platform for real-time search and characterization of protein surfaces. Methods in Molecular Biology, 1137, 105-117.
[BIBTEX] [PUBMED]
Kim, S., Sael, L., & Yu, H. (2014). Identifying cancer subtypes based on somatic mutation profile. In Proceedings of the ACM 8th International Workshop on Data and Text Mining in Bioinformatics - DTMBIO (pp. 19-22). New York, NY, USA: ACM.
[PDF] [BIBTEX]

2013

Pi, J., & Sael, L.* (2013). Mass spectrometry coupled experiments and protein structure modeling methods. International Journal of Molecular Sciences, 14(10), 20635-20657.
[PDF] [BIBTEX] [PUBMED]
Kim, S., Sael, L., & Yu, H. (2013). Efficient protein structure search using indexing methods. BMC Medical Informatics and Decision Making, 13(Suppl 1), S8.
[PDF] [BIBTEX] [PUBMED]
Tang, M., Tan, K. M., Tan, X. L., Sael, L., Chitale, M., Esquivel-rodriguez, J., & Kihara, D. (2014). Graphical models for protein function and structure prediction. In M. Elloumi & A. Y. Zomaya (Eds.), Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data (pp. 191-222). Hoboken, New Jersey: John Wiley & Sons, Inc.
[PDF] [BIBTEX]
Coelho, D., & Sael, L.* (2013). Breast and prostate cancer expression similarity analysis by iterative SVM based ensemble gene selection. In Proceedings of the 7th International Workshop on Data and Text Mining in Biomedical Informatics - DTMBIO (pp. 23-26). San Francisco, USA: ACM Press. [PDF] [BIBTEX]
Kim, S., Sael, L., & Yu, H. (2013). Efficient local ligand-binding site search using landmark MDS. In Proceedings of the 7th International Workshop on Data and Text Mining in Biomedical Informatics - DTMBIO (pp. 13-14). San Francisco, USA: ACM Press.
[PDF] [BIBTEX]
Kim, S., Sael, L., & Yu, H. (2013). Fast protein 3D surface search. In Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication - ICUIMC (pp. 1-8). Kota Kinabalu, Malaysia: ACM Press.
[PDF] [BIBTEX]

2012

Mullins, E. a, Starks, C. M., Francois, J. a, Sael, L., Kihara, D., & Kappock, T. J. (2012). Formyl-coenzyme A (CoA):oxalate CoA-transferase from the acidophile Acetobacter aceti has a distinctive electrostatic surface and inherent acid stability. Protein Science, 21(5), 686-696.
[BIBTEX] [PUBMED]
Sael, L.*, Chitale, M., & Kihara, D. (2012). Structure- and sequence-based function prediction for non-homologous proteins. Journal of Structural and Functional Genomics, 13(2), 111-23.
[BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2012). Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison. Proteins: Structure, Function, and Bioinformatics, 80(4), 1177-1195.
[BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2012). Constructing patch-based ligand-binding pocket database for predicting function of proteins. BMC Bioinformatics, 13(Suppl 2), S7. [BIBTEX] [PUBMED]
Kim, S., Sael, L., & Yu, H. (2012). Indexing methods for efficient protein 3D surface search. In Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics - [PDF] [BIBTEX]

2011

Kihara, D., Sael, L., Chikhi, R., & Esquivel-Rodriguez, J. (2011). Molecular surface representation using 3D Zernike descriptors for protein shape comparison and docking. Current Protein & Peptide Science, 12(6), 520-530.
[BIBTEX] [PUBMED]
Sael, L.* Structural bioinformatics and application to future drug design evolution. Communications of KIISE. 2011; 29:4, 26-32.
Chikhi, R., Sael, L., & Kihara, D. Protein binding ligand prediction using moment-based methods In D. Kihara (Ed.), Protein Function Prediction for Omics Era (pp. 145=163). Dordrecht: Springer Netherlands. 2011.
[BIBTEX] [ABSTRACT]

2010

Chikhi, R., Sael, L., & Kihara, D. (2010). Real-time ligand binding pocket database search using local surface descriptors. Proteins: Structure, Function, and Bioinformatics, 78(9), 2007-28.
[BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2010). Binding ligand prediction for proteins using partial matching of local surface patches. International Journal of Molecular Sciences, 11, 5009-5026.
[PDF] [BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2010). Improved protein surface comparison and application to low-resolution protein structure data. BMC Bioinformatics, 11(Suppl 1), S2. (GIW2010 - BEST PAPER AWARD)
[PDF] [BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2010). Characterization and classification of local protein surfaces using self-organizing map. International Journal of Knowledge Discovery in Bioinformatics, 1(1), 32-47.
[PDF] [BIBTEX] [ABSTRACT]

2009

La, D., Esquivel-rodriguez, J., Venkatraman, V., Li, B., Sael, L., Ueng, S., Steven, A., Kihara, D. (2009). 3D-SURFER: Software for high-throughput protein surface comparison and analysis. Bioinformatics, 25(21), 2843-4.
[PDF] [BIBTEX] [PUBMED]
Venkatraman, V., Yang, Y. D., Sael, L., & Kihara, D. (2009). Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinformatics, 10, 407.
[PDF] [BIBTEX] [Suppl. Data] [PUBMED]
Venkatraman, V., Sael, L., & Kihara, D. (2009). Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors. Cell Biochemistry and Biophysics, 54(1-3), 23-32.
[BIBTEX] [PUBMED]
Sael, L.*, & Kihara, D. (2009). Protein surface representation and comparison: New approaches in structural proteomics. In J. Y. Chen & S. Lonardi (Eds.), Biological Data Mining (pp. 89-109). USA: Chapman & Hall/CRC Press.
[PDF] [BIBTEX] [ABSTRACT]

2008

Sael, L.*, La, D., Li, B., Rustamov, R., & Kihara, D. (2008). Rapid comparison of properties on protein surface. Proteins: Structure, Function, and Bioinformatics, 73(1), 1-10.
[BIBTEX] [PUBMED]
Sael, L.*, Li, B., La, D., Fang, Y., Ramani, K., Rustamov, R., & Kihara, D. (2008). Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins: Structure, Function, and Bioinformatics, 72(4), 1259-1273.
[BIBTEX] [PUBMED]

ACADEMIC MEMBERSHIP

Board member of KIISE Artificial Intelligence Society (http://aisociety.kr) since 2013
Board member of Korean Society for Bioinformatics (KSBi) since 2015
Board member of KIISE Database Society of Korea since 2016
Member of International Society for Computational Biology (ISCB) since 2011
Member of Association for Computing Machinery (ACM) since 2004