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Pavel P. Kuksa
Research associate
Department of Pathology and Laboratory Medicine
Institute for Biomedical Informatics
Perelman School of Medicine
University of Pennsylvania
Publications:
See also my Google Scholar page
Journal Publications
- Pavel P. Kuksa, Chia-Lun Liu, Wei Fu, Liming Qu, Yi Zhao, Zivadin Katanic, Kaylyn Clark, Amanda B. Kuzma, Pei-Chuan Ho, Kai-Teh Tzeng, Otto Valladares, Shin-Yi Chou, Adam C. Naj, Gerard D. Schellenberg, Li-San Wang, and Yuk Yee Leung. Alzheimer's Disease Variant Portal: A Catalog of Genetic Findings for Alzheimer's Disease. Journal of Alzheimer's Disease, Preprint(Preprint):1–17, IOS Press, 2022.
Details
BibTeX
[pdf] [URL]
- Pavel P. Kuksa, Yuk Yee Leung, Prabhakaran Gangadharan, Zivadin Katanic, Lauren Kleidermacher, Alexandre Amlie-Wolf, Chien-Yueh Lee, Liming Qu, Emily Greenfest-Allen, Otto Valladares, and Li-San Wang. FILER: a framework for harmonizing and querying large-scale functional genomics knowledge. NAR Genomics and Bioinformatics, 4(1), jan 2022.
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[pdf] [URL]
- Pavel P. Kuksa, Chien-Yueh Lee, Alexandre Amlie-Wolf, Prabhakaran Gangadharan, Elizabeth E Mlynarski, Yi-Fan Chou, Han-Jen Lin, Heather Issen, Emily Greenfest-Allen, Otto Valladares, Yuk Yee Leung, and Li-San Wang. SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants. Bioinformatics, April 2020.
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BibTeX
[pdf] [URL]
- Pavel P. Kuksa, Alexandre Amlie-Wolf, Yih-Chii Hwang, Otto Valladares, Brian D. Gregory, and Li-San Wang. HIPPIE2: a method for fine-scale identification of physically interacting chromatin regions. NAR Genomics and Bioinformatics, 2020.
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[URL]
- Pavel P. Kuksa, Fan Li, Sampath Kannan, Brian D. Gregory, Yuk Yee Leung, and Li-San Wang. HiPR: High-throughput probabilistic RNA structure inference. Computational and Structural Biotechnology Journal, 18:1539 – 1547, 2020.
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[pdf] [URL]
- Pavel P Kuksa, Alexandre Amlie-Wolf, Zivadin Katanic, Otto Valladares, Li-San Wang, and Yuk Yee Leung. DASHR 2.0: integrated database of human small non-coding RNA genes and mature products. Bioinformatics, 35(6):1033–1039, Mar 2019.
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[pdf] [URL]
- Alexandre Amlie-Wolf, Mitchell Tang, Elisabeth E Mlynarski, Pavel P. Kuksa, Otto Valladares, Zivadin Katanic, Debby Tsuang, Christopher D Brown, Gerard D Schellenberg, and Li-San Wang. INFERNO: inferring the molecular mechanisms of noncoding genetic variants. Nucleic Acids Research, 46(17):8740–8753, 2018.
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[pdf] [URL]
- Pavel P Kuksa, Alexandre Amlie-Wolf, \v Zivadin Katani\'c, Otto Valladares, Li-San Wang, and Yuk Yee Leung. SPAR: small RNA-seq portal for analysis of sequencing experiments. Nucleic Acids Research, 46(W1):W36–W42, 2018.
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[pdf] [URL]
- Yuk Yee Leung*, Pavel P. Kuksa*, Alexandre Amlie-Wolf, Otto Valladares, Lyle H. Ungar, Sampath Kannan, Brian D. Gregory, and Li-San Wang. DASHR: database of small human noncoding RNAs. Nucleic Acids Research (Database Issue), 2016.
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[pdf] [URL]
- Lee E. Vandivier, Rafael Campos, Pavel P. Kuksa, Ian M. Silverman, Li-San Wang, and Brian D. Gregory. Chemical Modifications Mark Alternatively Spliced and Uncapped Messenger RNAs in Arabidopsis. The Plant Cell, 27(11):3024–3037, 2015.
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[pdf] [URL]
- Pavel P. Kuksa, Martin Renqiang Min, Rishabh Dugar, and Mark Gerstein. High-order neural networks and kernel methods for peptide-MHC binding prediction. Bioinformatics, 31(22):3600–3607, 2015.
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[pdf] [URL]
- Shawn W Foley, Lee E Vandivier, Pavel P Kuksa, and Brian D Gregory. Transcriptome-wide measurement of plant \RNA secondary structure. Current Opinion in Plant Biology, 27:36 – 43, 2015. Cell signalling and gene regulation
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BibTeX
[pdf] [URL]
- Yih-Chii Hwang, Chiao-Feng Lin, Otto Valladares, John Malamon, Pavel Kuksa, Qi Zheng, Brian D. Gregory, and Li-San Wang. HIPPIE: A high-throughput identification pipeline for promoter interacting enhancer elements. Bioinformatics, 2014.
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[URL]
- Pavel P. Kuksa. Biological Sequence Analysis with Multivariate String Kernels. IEEE/ACM Transactions on Computational Biology and Bioinformatics, March 2013.
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- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Efficient use of unlabeled data for protein sequence classification: a comparative study. BMC Bioinformatics, 10(Suppl 4):S2, 2009. Impact factor: 3.78
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[URL]
- Pavel Kuksa and Vladimir Pavlovic. Efficient alignment-free DNA barcode analytics. BMC Bioinformatics, 10(Suppl 14):S9, 2009. Impact factor: 3.78
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BibTeX
[pdf]
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[URL]
[Supplementary Material]
- Pavel Kuksa and Vladimir Pavlovic. Efficient motif finding algorithms for large-alphabet inputs. BMC Bioinformatics, 11(Suppl 8):S1, 2010.
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BibTeX
[pdf]
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[URL]
- Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning, 12:2493–2537, 2011.
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Refereed conferences
- Pavel P. Kuksa, Prabhakaran Gangadharan, Chien-Yueh Lee, Yi-Fan Chou, Emily Greenfest-Allen, Han-Jen Lin, Z. Katanic, Otto Valladares, Yuk Yee Leung, and Li-San Wang. GADB: Large-scale, curated Functional Genomics Annotation Database. In American Society of Human Genetics Annual Meeting (ASHG), 2019.
Details
BibTeX
(unavailable)
- C.-Y. Lee*, Pavel P. Kuksa*, A. Amlie-Wolf, E.E. Mlynarski, Y.-F. Chou, H.-J. Lin, E. Greenfest-Allen, Z. Katanic, O. Valladares, A. Kuzma, A. Naj, G.D. Schellenberg, Y.Y. Leung, L.-S. Wang, and Alzheimer's Disease Sequencing Project. INFERNO2: Scalable Spark-based framework for inferring dysregulated enhancer and noncoding RNAs for WGS and GWAS data. In American Society of Human Genetics Annual Meeting (ASHG), 2019.
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(unavailable)
- Y.Y. Leung, Pavel P. Kuksa, C.-Y. Lee, Y.-F. Chou, A. Amlie-Wolf, G.D. Schellenberg, and L.-S. Wang. Non-coding regulatory landscape of Alzheimer's disease variants using GWAS of 63,926 individuals.. In American Society of Human Genetics Annual Meeting (ASHG), 2019.
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(unavailable)
- Pavel P. Kuksa*, A. Amlie-Wolf*, Y.-C. Hwang*, B. D. Gregory, and L.-S. Wang. Hi-C-based characterization of the landscape of physically interacting regions and interaction mechanisms across six human cell lines using HiPPIE2. In American Society of Human Genetics Annual Meeting (ASHG), 2018.
Details
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(unavailable)
- E.E. Mlynarski, A. Amlie-Wolf, Pavel P. Kuksa, O. Valladares, G.D. Schellenberg, and L.-S. Wang. SV-INFERNO: A Spark based pipeline for INFERring the molecular mechanisms of NOncoding structural variants. In American Society of Human Genetics Annual Meeting (ASHG), 2018.
Details
BibTeX
(unavailable)
- Pavel P. Kuksa, Y.Y. Leung, A. Amlie-Wolf, O. Valladares, and L.-S. Wang. DASHR 2.0: Database of small non-coding RNAs in normal human tissues and cell types. In American Society of Human Genetics Annual Meeting (ASHG), 2017.
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- Y.Y. Leung*, Pavel P. Kuksa*, A. Amlie-Wolf, and L.-S. Wang. The landscape of short RNAs in human cell types and tissues. In American Society of Human Genetics Annual Meeting (ASHG), 2017. Top 10% Reviewers Choice
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- A. Amlie-Wolf, M. Tang, Pavel P. Kuksa, Y.Y. Leung, B. Slaff, J. King, B. Dombroski, G.D. Schellenberg, and L.-S. Wang. INFERNO -- INFERring the molecular mechanisms of NOncoding genetic variants. In American Society of Human Genetics Annual Meeting (ASHG), 2016.
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(unavailable)
- Y.Y. Leung, Pavel P. Kuksa, A. Amlie-Wolf, and L.-S. Wang. The landscape of regulatory post-transcriptionally derived small non-coding RNAs in the human transcriptome. In American Society of Human Genetics Annual Meeting (ASHG), 2016.
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(unavailable)
- Pavel P. Kuksa, Martin Renqiang Min, Rishabh Dugar, and Mark Gerstein. High-order neural networks and kernel methods for MHC-peptide binding prediction. In NIPS Machine Learning in Computational Biology, 2014.
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(328.0kB
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- Y.-C. Hwang*, P. P. Kuksa*, B. D. Gregory, L.-S. Wang. Identifying the transcription factors mediating enhancer--target gene regulation in the human genome. In American Society of Human Genetics Annual Meeting (ASHG), 2015. (Platform talk)
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[URL]
- Mitchell Tang, Christian Kramer, George Xu, Michele Hawk, Yih-Chii Hwang, Chiao-Feng Lin, Pavel Kuksa, Weixin Wang, Beth A. Dombroski, Adam C. Naj, Li-San Wang, Gerald D. Schellenberg. Prediction of Late-Onset Alzheimer's Disease Associated Enhancer Elements. In Alzheimer's Association International Conference (AAIC), 2015.
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[URL]
- P. P. Kuksa, Y. Y. Leung, A. Amlie-Wolf, B. D. Gregory, L.-S. Wang. SPAR: Sequencing-based pipeline for annotating novel small non-coding RNAs. In American Society of Human Genetics Annual Meeting (ASHG), 2015.
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[URL]
- Y. Y. Leung*, P. P. Kuksa*, A. Amlie-Wolf, O. Valladares, B. D. Gregory, L.-S. Wang. DASHR - Database of small human non-coding RNAs. In American Society of Human Genetics Annual Meeting (ASHG), 2015.
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[URL]
- Pavel P. Kuksa. Efficient multivariate sequence classification. In CoRR abs/1409.8211, 2013.
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- Pavel P. Kuksa and Vladimir Pavlovic. Efficient evaluation of large sequence kernels. In KDD, 2012 (oral presentation). Acceptance rate: 133/755 (17.6%)
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[pdf]
(303.6kB
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- Pavel P. Kuksa, Imdadullah Khan, and Vladimir Pavlovic. Generalized Similarity Kernels for Efficient Sequence Classification. In SDM, 2012. Acceptance rate: 99/362 (27%)
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(315.8kB
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- Pavel P. Kuksa. Efficient sequence kernel-based genome-wide prediction of transcription factors. In ICPR, 2012.
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- Pavel P. Kuksa. 2D similarity kernels for biological sequence classification. In BIOKDD, 2012.
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- Pavel P. Kuksa. Efficient time series classification with Multivariate similarity kernels. In NYAS Machine Learning Symposium, 2012. Oral presentation
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(180.7kB
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- Pavel Kuksa and Yanjun Qi. Semi-Supervised Bio-Named Entity Recognition with Word-Codebook Learning. In SDM, 2010. Acceptance rate: 82/351 (23%)
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[pdf]
(394.1kB
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- Pavel P. Kuksa, Yanjun Qi, Bing Bai, Ronan Collobert, Jason Weston, Vladimir Pavlovic, and Xia Ning. Semi-Supervised Abstraction-Augmented String Kernel for Multi-Level Bio-Relation Extraction. In ECML, 2010. Acceptance rate: 106/658 (16%)
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- Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kavukcuoglu, and Jason Weston. Combining labeled and unlabeled data with word-class distribution learning. In Proceeding of the 18th ACM Conference on Information and Knowledge Management CIKM 2009, pp. 1737–1740, 2009. Acceptance rate: (123+171)/847 (20% short paper)
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[URL]
- Yanjun Qi, Pavel P. Kuksa, Ronan Collobert, Kunihiko Sadamasa, Koray Kavukcuoglu, and Jason Weston. Semi-Supervised Sequence Labeling with Self-Learned Features. In Proc. International Conference on Data Mining (ICDM'09), IEEE, 2009. Acceptance rate: 8.9% regular (70/786)
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BibTeX
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(231.8kB
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- Pavel P. Kuksa and Vladimir Pavlovic. Efficient Motif Finding Algorithms for Large-Alphabet Inputs. In BIOKDD, 2010. Acceptance rate: 7/29 regular (24%)
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[pdf]
(442.7kB
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- Pavel Kuksa and Vladimir Pavlovic. Fast motif selection for biological sequences. In IEEE International Conference on Bioinformatics and Biomedicine BIBM'09, 2009. Acceptance rate: (44+37)/233 (35%)
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BibTeX
[pdf]
(159.6kB
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[URL]
- Pavel P. Kuksa and Vladimir Pavlovic. Spatial Representation for Efficient Sequence Classification. In ICPR, 2010. Acceptance rate: 385/2140 oral (18%)
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(144.0kB
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- Pavel Kuksa and Vladimir Pavlovic. Efficient Alignment-free Barcode Analytics. In Third International Barcode of Life Conference, 2009.
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[URL]
- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Scalable Algorithms for String Kernels with Inexact Matching. In NIPS, 2008. Spotlight Presentation. Acceptance rate: 123/1022 (12%)
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[pdf]
(111.0kB
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[supplementary data]
- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. On the role of local matching for efficient semi-supervised protein sequence classification. In BIBM, 2008. Acceptance rate: 38/156 (24%)
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BibTeX
[pdf]
(133.0kB
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- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. A fast, semi-supervised learning method for protein sequence classification. In 8th International Workshop on Data Mining in Bioinformatics (BIOKDD 2008), pp. 29–37, 2008. Acceptance rate: 8/25 (32%)
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(197.0kB
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- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Fast and Accurate Multi-class Protein Fold Recognition with Spatial Sample Kernels. In Computational Systems Bioinformatics: Proceedings of the CSB2008 Conference, pp. 133–143, 2008. Acceptance rate: 30/135 (22%)
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(399.1kB
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[supplementary materials]
- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Fast Protein Homology and Fold Detection with Sparse Spatial Sample Kernels. In 19th International Conference on Pattern Recognition ICPR 2008, 2008. Acceptance rate: 18% (oral). Best paper nominee
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[pdf]
(89.0kB
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[supplementary materials]
- Pavel Kuksa and Vladimir Pavlovic. Fast Barcode-Based Species Identification Using String Kernels. In Second International Barcode of Life Conference, 2007. Acceptance rate: 30% (oral)
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[URL]
- Pavel Kuksa and Vladimir Pavlovic. Fast Kernel Methods for SVM Sequence Classifiers. In WABI, pp. 228–239, 2007. Acceptance rate: 37/131 (28%)
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(145.2kB
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Workshop papers
- Pavel P. Kuksa. Using string kernels to predict gene expression. Snowbird Learning Workshop, Snowbird, Utah, April 2012, 2012.
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- Pavel P. Kuksa. 2D similarity kernels and representations for sequence data. Snowbird Learning Workshop, Snowbird, Utah, April 2012, 2012.
Details
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(225.4kB
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- Pavel Kuksa and Vladimir Pavlovic. Efficient evaluation of large sequence kernels. In NYAS Machine Learning Symposium, 2011.
Details
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(203.4kB
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- Pavel Kuksa and Vladimir Pavlovic. Efficient Sequence Classification with Spatial Representations. In Snowbird Learning Workshop, April 2010. Oral presentation.
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[pdf]
(22.9kB
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- Vladimir Pavlovic and Pavel Kuksa. Large scale sequence analytics. In Center for Dynamic Data Analytics (CDDA) Workshop (January 25-26, 2010), 2010.
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[URL]
- Jason Weston, Ronan Collobert, Frederic Ratle, Hossein Mobahi, Pavel Kuksa, and Koray Kavukcuoglu. Deep Learning via Semi-Supervised Embedding. In ICML 2009 Workshop on Learning Feature Hierarchies, 2009.
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[URL]
- Pavel Kuksa and Vladimir Pavlovic. Efficient Discovery of Common Patterns in Sequences. Snowbird Learning Workshop, Clearwater, Florida, April 13-16 2009, 2009.
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[pdf]
(100.8kB
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- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. High Performance Sequence Classification with Novel Spatial Sample Embedding. 3rd Annual Machine Learning Symposium, NY, Oct 10, 2008, 2008.
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[URL]
- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Spatially-constrained sample kernel for sequence classification. Snowbird Learning Workshop, Utah, April 1-4, 2008, 2008.
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[pdf] (38.6kB) [poster]
- Pavel Kuksa and Vladimir Pavlovic. Kernel methods for DNA barcoding. Snowbird Learning Workshop, San Juan, Puerto Rico, March 2007, 2007.
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[URL]
Technical Reports
- Pavel Kuksa and Vladimir Pavlovic. Sublinear selection algorithms for motif finding. DIMACS, 2010.
[pdf]
- Pavel Kuksa and Vladimir Pavlovic. Efficient discovery of common patterns in sequences over large alphabets. Technical Report 2009-15, DIMACS, 2009.
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- Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Kernel Methods and Algorithms for General Sequence Analysis. Technical Report DCS-TR-630, Rutgers University, 2008.
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[URL]
- Pai-Hsi Huang, Pavel Kuksa, and Vladimir Pavlovic. Fast and accurate semi-supervised protein homology detection with large uncurated sequence databases. Technical Report RU-DCS-TR634, Rutgers University, 2008.
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[pdf]
(218.6kB
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- Robert S. Moore, Richard Howard, Pavel Kuksa, and Richard P. Martin. A Geometric Approach to Device-Free Motion Localization Using Signal Strength. Technical Report DCS-TR-674, Rutgers University, 2010.
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[pdf]
(456.2kB
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- Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural Language Processing (almost) from Scratch. arXiv:1103.0398v1, 2011.
Details
BibTeX
[pdf]
(726.8kB
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- Pavel P. Kuksa, Imdadullah Khan, and Vladimir Pavlovic. Generalized Similarity Kernels for Efficient Sequence Classification. Technical Report RU-DCS-TR684, Rutgers University, 2011.
Details
BibTeX
[pdf]
(164.2kB
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Theses
Invited Lectures and Talks
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Multivariate and generalized similarity kernels for sequence analysis. New Jersey Institute of Technology. March 7, 2013 (invited lecture)
- String kernel-based species identification using DNA barcodes. Joint Molecular Biosciences Symposium, Feb 29, 2008 (invited talk).
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BibTeX
[URL]
- Kernel Methods for DNA Barcoding. Rutgers Bioinformatics Meeting, Nov 11, 2006 (invited talk).
Details
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[pdf]
(634.1kB
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- Efficient time series classification with multivariate similarity kernels. NYAS Machine Learning Symposium. New York, NY, Oct. 19, 2012 (oral presentation)
- Efficient evaluation of large sequence kernels. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Aug. 14, 2012 (oral presentation)
- Fast and accurate multi-class protein fold recognition with spatial sample kernels. Computational Systems Bioinformatics (CSB). Stanford University, Aug. 27, 2008 (oral presentation)
- Fast protein homology and fold detection with sparse spatial sample kernels. International Conference on Pattern Recognition (ICPR). Tampa, FL, Dec. 9, 2008 (oral presentation)
- A fast, semi-supervised learning method for protein sequence classification. International Workshop on Data Mining in Bioinformatics. Las Vegas, Aug. 24, 2008 (oral presentation)
- On the role of local matching for efficient semi-supervised protein sequence classification. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Philadelphia, Nov. 4, 2008 (oral presentation)
- Fast kernel methods for SVM sequence classifiers. International Workshop on Algorithms in Bioinformatics (WABI). University of Pennsylvania, Sept. 8, 2007 (oral presentation)
- Efficient motif finding algorithms for large-alphabet inputs. International Workshop on Data Mining in Bioinformatics (BIOKDD). Washington, DC, July 25, 2010 (oral presentation)
- Efficient sequence classification with spatial representations. Snowbird Learning Workshop, Utah, Apr. 8, 2010 (oral presentation)
Other Presentations
- Pavel P. Kuksa. Scalable Kernel Methods and Algorithms for General Sequence
Analysis. Ph.D. Defense, March 30, 2011 [pdf]
- Pavel P. Kuksa. Scalable Kernel Methods and Algorithms for General Sequence
Analysis. Ph.D. Predefense, Oct 19, 2010 [pdf]
- Pavel Kuksa. Kernel Methods and Algorithms for General Sequence
Analysis. Ph.D. Qualifying Exam Presentation [pdf]
Publications in Russian
- Kouxa, Pavel. "Applications of Associative Memory and Associative
Processors in Network Devices: A Survey." (Primenenie associativnih ZU
i processorov v setevih ustroistvah) Modern Information Technologies.
Moscow: BMSTU Press, 2001. 20-24, ISBN 5-7038-2083-9
[pdf]
- Kouxa, Pavel. "Data Clustering with Neural Networks."
(Primenenie neironnih setei dlya klasterizatsii dannih) Modern
Information Technologies. Moscow: BMSTU Press, 2001. 7-13, ISBN
5-7038-2083-9
[pdf]
- Kouxa, Pavel. "Finding Logically Independent Operators
Matrix." (Algoritm nahojdeniya matricy logicheskoi nesovmestimosti)
Informatics and Control Systems in 21st century. Moscow: Eliks+ Press,
2002. 195-97, ISBN 5-93991-010-6
[pdf]
- Kouxa, Pavel. "Fuzzy Systems Simulation Software at
Algorithmic Level." (Sistema modelirovaniya nechetkih system na
algoritmicheskom urovne) Informatics and Control Systems in 21st
century. Moscow, 2002. 201-04, ISBN 5-93991-010-6
[pdf]
- Kouxa, Pavel. "Finding Logically Independent Operators for
Parallel Computations Scheduling." (Vychislenie matritsy logicheskoi
nesovmestimosti pri organizatsii parallel'nyh vychislenii) Young
Scientists, Graduate and Undergraduate Students Conference. Moscow:
BMSTU Press, 2003. 293-97, ISBN 5-7038-2347-1
[pdf]
- Kouxa, Pavel. "Fuzzy Clustering Algorithm: Reducing
Complexity." (Analiz algoritma nechetkoi klasterizatsii) Young
Scientists, Graduate and Undergraduate Students Conference. Moscow:
BMSTU Press, 2003. 249-53, ISBN 5-7038-2347-1
[pdf]
- Kouxa, Pavel. "Fuzzy Systems Definition Language." (Yazyk
opisaniya nechetkih sistem) Journal of Volzskiy Tatishev University,
Information Science Series.5 (2004): 92-97.
[pdf]
- Kouxa, Pavel. "Linguistic Fuzzy Models Structure
Identification and Optimization." (Strukturnaya identificatsiya i
opptimizatsiya lingvisticheskih nechetkih modelei) Modern Information
Technologies. Moscow: Eliks+, 2003. 7-16, ISBN 5-93991-014-9
[pdf]
- Kouxa, Pavel. "Neuro-Fuzzy Models: Analysis and Applications."
(Analiz neiro-nechetkih modelei) Journal of Volzskiy Tatishev
University, Information Science Series.5 (2004): 69-76.
[pdf]
- Kouxa, Pavel. "Software Implementation of Fuzzy Logic
Controllers." (Programmnaua realizatsiya nechetcih kontrollerov) Modern
Information Technologies. Moscow: Eliks+, 2003. ISBN 5-93991-014-9
[pdf]
- Kouxa, Pavel. "Type-2 Fuzzy Models." (Nechetkie
lingvisticheskie modeli vtorogo roda) Journal of Volzskiy Tatishev
University, Information Science Series.5 (2004): 114-20.
[pdf]
- Kouxa, Pavel. "Type-2 Fuzzy Sets Algebra." (Issledovanie
operacii algebry nechetkih mnojestv vtorogo poryadka) Young Scientists,
Graduate and Undergraduate Students Conference, Informatics and Control
Systems. Moscow: BMSTU Press, 2003. 243-48, ISBN 5-7038-2347-1
[pdf]
- Kouxa, Pavel. "Approximation Accuracy of Fuzzy Systems."
(Obespechenie tochnosti v nechetkih sistemah) Young Scientists,
Graduate and Undergraduate Students Conference. Informatics and Control
Systems in 21st century. Moscow: BMSTU Press, 2004. 152-156, ISBN
5-7038-2646-4
[pdf]
- Kouxa, Pavel. "Design Optimization of Non-Local Neuro-Fuzzy
Models." (Optimizatsiya i sintez nelokal'nyh lingvisticheskih
neiro-nechetkih modelei) Inter-universities Scientific and Technical
Conference on �Modern Information Technologies�. Moscow, 2004. (in
press)
[pdf]
- Kouxa, P. "Fuzzy Systems: Learning and Self-Organization
Methods." Young Scientists, Graduate and Undergraduate Students
Conference. Informatics and Control Systems in 21st century. Moscow:
BMSTU Press, 2004. 148-52, ISBN 5-7038-2646-4
[pdf]
- Kouxa Pavel, Schmakov E., Yahina I., Panushkin M. "A Survey on
Supercomputing: Modern State and Perspectives." (Superkomputery:
sostoyanie i perspektivy) Anniversary Inter-universities Scientific and
Technical Conference on "Modern Information Technologies". Moscow:
BMSTU Press, 2000. 61-68, ISBN 5-7038-1752-8
[pdf]
- Timofeev V.V., Kouxa P. "Design and Identification of Fuzzy
Models in Control and Systems Modeling." (Postroenie i identifikatsiya
nechetkih modelei v prilojeniyah upravlenia i sistemnogo
modelirovaniya) First International Conference "Aerospace
Technologies". Moscow: BMSTU Press, 2004. ISBN 5-7038-2517-2
[pdf]