Publications

The list is not frequently updated. Please visit Google Scholar(Lee Sael, Soon-Sun Kwon) for more up-to-date list.

Conferences (14)

  1. JG Jang, M Park, J Lee, L Sael (2022) Large-scale Tucker tensor factorization for sparse and accurate decomposition. The Journal of Supercomputing (SUPE), 1-31

  2. J Lee, M Choi, L Sael, H Shim, J Lee (2022) Knowledge distillation meets recommendation: collaborative distillation for top-N recommendation Knowledge and Information Systems 64 (5), 1323-1348

  3. JJ Park, L Sael (2022) Benchmarking Deep Graph Models for Large Molecular Generation 2022 IEEE International Conference on Big Data and Smart Computing (BigComp22)

  4. S Heesoo, L Sangseok, L Sael (2022) Cross-Attention Model for Multi-modal Bio-Signal Processing. 2022 IEEE International Conference on Big Data and Smart Computing (BigComp22)

  5. J Yoo, L Sael (2022) Transition Matrix Representation of Trees with Transposed Convolutions. SIAM International Conference on Data Mining (SDM22)

  6. J Yoo, L Sael (2021) Gaussian Soft Decision Trees for Interpretable Feature-Based Classification. PAKDD 2, 143-155

  7. Yoo J. & Sael, L (2019) EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees. In the IEEE International Conference on Data Mining (ICDM 2019). Beijing, China. (BKCSA061 IF=3)

  8. Oh, S., Park, N., Sael, L., & Kang, U., (2018) Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries. In 34th IEEE International Conference on Data Engineering (ICDE 2018). (BKCS0051 IF=3)

  9. Thomas, J., Thomas, S., Sael, L. (2017) DP-miRNA: An Improved Prediction of precursor microRNA using Deep Learning Model. BIGCOMP 2017. Jeju, Korea.

  10. Jung, Jinhong, Namyong Park, Lee Sael, and U Kang. 2017. “BePI: Fast and Memory-Efficient Method for BillionScale Random Walk with Restart.” In ACM International Conference on Management of Data (SIGMOD 2017). Raleigh, North Carolina, USA.: ACM Press. (BKCSA028 IF=4)

  11. 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. BEST PAPER (Runner-Up)

  12. 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 2016). (BKCS0051 IF=3)

  13. Thomas, J., & Sael, L. (2015). Overview of integrative analysis methods for heterogeneous data. In 2015 International Conference on Big Data and Smart Computing (BIGCOMP) (pp. 266–270).

  14. Shin, Kijung, Jinhong Jung, Lee Sael, and U Kang. (2015). “BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs.” In Proceedings of the 2015 ACM International Conference on Management of Data (SIGMOD 2015), 1571–85. Melbourne, Victoria, Australia: ACM Press. (BKCSA028 IF=4)

  15. 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 2014). Shanghai, China: ACM. (BKCS0027 IF=3)

  16. Kim, S., Sael, L., & Yu, H. (2014). Identifying cancer subtypes based on somatic mutation profile. In Proceedings of the 8th International Workshop on Data and Text Mining in Biomedical Informatics - DTMBIO’14. Shanghai, China: ACM.

  17. 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 ’13 (pp. 23–26). San Francisco, USA: ACM.

  18. Kim, S., Sael, L., & Yu, H. (2013) Efficient local ligand-binding site search using landmark MDS. Proceedings of the 7th International Workshop on Data and Text Mining in Biomedical Informatics – DTMBIO’13. San Francisco, USA: ACM.

  19. Kim, S., Sael, L., & Yu, H. (2013) Fast protein 3D surface search. Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication - ICUIMC’13 (pp. 1–8). Kota Kinabalu, Malaysia: ACM.

  20. Kim, S., Sael, L., & Yu, H. (2012). Indexing methods for efficient protein 3D surface search. In Proceedings of the ACM 6th International Workshop on Data and Text Mining in Biomedical informatics - DTMBIO ’12 (p. 41). New York, New York, USA: ACM

SCI(E) Journals (28)

  1. E Cho, H Lee, L Sael, et al. (2022) A neural network model for free-falling condensation heat transfer in the presence of non-condensable gases. International Journal of Thermal Sciences 171, 107202

  2. JY Park, S Wong, L Sael, et al. (2021) A missense variant in SHARPIN mediates Alzheimer’s disease-specific brain damages Translational psychiatry 11 (1), 1-9

  3. H Lee, H Lee, L Sael, et al. (2021) An artificial neural network model for predicting frictional pressure drop in micro-pin fin heat sink. Applied Thermal Engineering 194, 117012

  4. K Lee, G Hong, L Sael, S Lee, HY Kim (2020) MultiDefectNet: Multi-class defect detection of building façade based on deep convolutional neural network. Sustainability 12 (22), 9785

  5. YJ Choi, DH Lee, L Sael, 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

  6. HK Shin, SW Lee, GP Hong, L Sael, SH Lee, HY Kim (2020) Defect-detection model for underground parking lots using image object-detection method. Computers, Materials and Continua 66 (3), 2493-2507

  7. J Jung, L Sael (2020) Fast and accurate pseudoinverse with sparse matrix reordering and incremental approach. Machine Learning 109, 2333–2347.

  8. Choi, D. & Sael, L. (2019). SNeCT: Integrative cancer data analysis via large scale network constrained tensor decomposition. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) . (SCIE Q1; top 9.35%; IF: 2.428)

  9. Lim, Y., Yu, I., Seo,D., Kang, U & Sael, L. (2019) PS-MCL: Parallel Shotgun Coarsened Markov Clustering of Protein Interaction Networks. BMC Bioinformatics. (SCIE Q1; top 22.88%; IF: 2.213)

  10. 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): 2237–48. (SCI Q1; top 10.4%; IF=3.402)

  11. 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. (SCI Q1; top 2.6%; IF=7.307)

  12. Lee, J., Choi, D., & Sael, L. (2018). CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions. PLOS ONE 13(7): e0200579. (SCIE Q1; top 22.7%; IF=2.766)

  13. Dehiya, V., & Sael, L. (2018). Impact of structural prior knowledge in mutation prioritization: Towards causal variant finding in rare disease. PLOS ONE 13(9):e0204101. (SCIE Q1; top 22.7%; IF=2.766)

  14. Thomas, J., and 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) 150-166. (SCIE Q4; IF=0.624)

  15. 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. (SCI Q1; top 14.0%; IF=3.438)

  16. Thomas, J., Seo, D., & Sael, L. (2016). Review on graph clustering and subgraph similarity based analysis of neurological disorders. International Journal of Molecular Sciences (IJMS), 17(6), 862. (SCIE Q2; top 32.2%; IF=3.226)

  17. Jeon, I., Papalexakis, E. E., Faloutsos, C., Sael, L, Kang, U. (2016). Mining billion-scale tensors: algorithms and discoveries. The VLDB Journal, 1-26. (SCI Q1; top 5.1%; IF=4.269)

  18. Jung, J., Shin, K., Sael, L., & Kang, U. (2016). Random walk with restart on large graphs using block elimination. ACM Transactions on Database Systems (TODS), 41(2), 1–43. (SCI)

  19. 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. (SCI Q1; top 2.6%; IF=7.307 )

  20. 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 (IJDMB), 12(4), 417–433. (SCIE Q4; IF= 0.624)

  21. Pi, J., Sael, L. (2013) Mass spectrometry coupled experiments and protein structure modeling methods. International Journal of Molecular Sciences, 14:10, 20635-20657. (SCIE Q2; top 32.2%; IF=3.226)

  22. Kim, S., Sael, L., Yu, H. (2013). Efficient protein structure search using indexing methods. BMC Medical Informatics and Decision Making, 13 (Suppl 1), S8. (SCIE Q2; IF=1.496).

  23. 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. (SCI Q3; IF=2.735)

  24. Sael, L., Chitale, M., & Kihara, D. (2012). Structure and sequence-based function prediction for non-homologous proteins. Journal of Structural and Functional Genomics, 13:111-123.

  25. Sael, L., & Kihara, D. (2012). Detecting local ligand-binding site similarity in non-homologous proteins by surface patch comparison. Proteins: Structure, Function, and Bioinformatics, 80:4, 1177-1195. (SCI Q2; IF=3.337)

  26. Sael, L., & Kihara, D. (2012). Constructing patch-based ligand-binding pocket database for predicting function of proteins. BMC Bioinformatics, 13(Suppl 2):S7. (SCIE Q1; IF=3.024)

  27. 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 and Peptide Science, 12:6, 520-530.(SCIE Q2; IF=2.886)

  28. 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, 2007-2028. (SCI Q2; IF=3.337)

  29. Sael, L., & Kihara, D. (2010). Binding ligand prediction for proteins using partial matching of local surface patches. International Journal of Molecular Sciences, 11:12, 5009-5026. (SCIE Q2; top 32.2%; IF=3.226)

  30. Sael, L., & Kihara, D. (2010). Improved protein surface comparison and application to low-resolution protein structure data. BMC Bioinformatics, 11 (Suppl 11): S2, 1-12. (SCIE Q1; IF=3.024) GIW2010 - BEST PAPER AWARD

  31. La, D., Esquivel, J., Venkatraman, V., Li, B., Sael, L., Ueng, S., Ahrendt, S., & Kihara, D. (2009). 3D-SURFER: software for high-throughput protein surface comparison and analysis. Bioinformatics, 25:21, 2843-2844. (SCI Q1; IF=5.766)

  32. 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. (SCIE Q2; IF=3.337)

  33. Venkatraman, V., Yang, Y., Sael, L., & Kihara, D. (2009). Protein-protein docking using region-based 3D Zernike descriptors. BMC Bioinformatics, 10:407, 1-21. (SCIE Q1; IF=3.024)

  34. Sael, L., La, D., Li, B., Rustamov, R., & Kihara, D. (2008). Rapid comparison of properties on protein surface. Proteins 73:1, 1-10. (SCI Q2; IF=3.337)

  35. 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, 72:4, 1259-1273. (SCI Q2; IF=3.337)

Book Chapters (4)

  1. 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.

  2. Tang, M., Tan, K. M., Tan, X. L., Sael, L., Chitale, M., Esquivel-Rodríguez, J., & Kihara, D. (2013). 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.

  3. Chikhi, R., Sael, L., & Kihara, D. (2011). Protein binding ligand prediction using moment-based methods. In D. Kihara (Ed.), Protein Function Prediction for Omics Era (pp. 145–163). Dordrecht: Springer Netherlands.

  4. 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

Patents (1)