Pavel P. Kuksa's Publications

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• 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.
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• 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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• 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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• Emily Greenfest-Allen, Conor Klamann, Elizabeth E. Mlynarski, Pavel P. Kuksa, Prabhakaran Gangadharan, Amanda B Kuzma, Otto Valladares, Yuk Yee Leung, Christian J Stoeckert Jr., and Li-San Wang. Introducing the NIAGADS Alzheimer's GenomicsDB API: a toolkit for remote exploration of Alzheimer's disease genetics. In Alzheimer's Association International Conference (AAIC), 2021.
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• Matei Ionita, Pavel P. Kuksa, Chixiang Chen, Yuk Yee Leung, and Li-San Wang. Dissecting multiple-signal GWAS loci and their regulatory roles in Alzheimer's Disease. In American Society of Human Genetics Annual Meeting (ASHG), 2021.
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• Pavel. P. Kuksa, P. Gangadharan, L. Kleidermacher, E. Greenfest-Allen, C.-Y. Lee, H.-J. Lin, Y.-F. Chou, Z. Katanic, O. Valladares, Y.Y. Leung, and L.-S. Wang. FILER: Harmonized, scalable Functional genomics repository and API. In ENCODE 2020 Research Applications & Users meeting, 2020. Oral presentation. [https://www.youtube.com/watch?v=dG0-iziv7o4]
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• Yuk Yee Leung, Pavel P. Kuksa, Yi-Fan Chou, Chia-Lun Liu, Wei Fu, Liming Qu, Yi Zhao, Zivadin Katanic, Amanda B Kuzma, Pei-chuan Ho, Kai-Teh Tzeng, Otto Valladares, Shin-Yi Chou, Adam C Naj, and Gerald D. Schellenberg. Characterization of regulatory roles of genetics signals curated from >200 GWA studies in Alzheimer's Disease Variant Portal (ADVP). In Alzheimer's Association International Conference (AAIC), 2021.
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• Amlie-Wolf A., Kuksa Pavel P., Lee C.-Y., Mlynarski E., Leung Y.Y., and Wang L.-S.. Using INFERNO to Infer the Molecular Mechanisms Underlying Noncoding Genetic Associations. In Haiming Cao, editor, Functional Analysis of Long Non-Coding RNAs, Methods in Molecular Biology, Humana, New York, NY, 2021.
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• Kuksa Pavel P., Lee C.-Y., Gangadharan P., Valladares O., Leung YY, and Wang L.-S.. SparkINFERNO: next-generation high-throughput pipeline for inferring molecular mechanisms of non-coding genetic associations. In Program in Quantitative Genomics (PQG), 2020.
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• Pavel. P. Kuksa, P. Gangadharan, L. Kleidermacher, E. Greenfest-Allen, C.-Y. Lee, H.-J. Lin, Y.-F. Chou, Z. Katanic, O. Valladares, Y.Y. Leung, and L.-S. Wang. FILER: Integrated, large-scale Functional genomics repository. In American Society of Human Genetics Annual Meeting (ASHG), 2020.
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• 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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• 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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• 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.
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• 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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• 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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• 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.
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• 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.
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• A. Amlie-Wolf, L. Qu, E.E. Mlynarski, Pavel P. Kuksa, Y.Y. Leung, C.D. Brown, G.D. Schellenberg, and L.S. Wang. Inferring enhancer and noncoding RNA dysregulation underlying 2,419 UK Biobank phenotypes. In American Society of Human Genetics Annual Meeting (ASHG), 2018. Platform talk
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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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• 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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• 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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• 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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• 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, 05 2018.
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• 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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• 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, 08 2018.
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• Pavel P. Kuksa, Yuk Yee Leung, Lee E. Vandivier, Zachary Anderson, Brian D. Gregory, and Li-San Wang. In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR, pp. 211–229, Springer New York, New York, NY, 2017.
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• Y. Y. Leung, P. P. Kuksa, A. Amlie-Wolf, O. Valladares, B. D. Gregory, and 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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• P. P. Kuksa, Y. Y. Leung, A. Amlie-Wolf, B. D. Gregory, and 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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• Y.-C. Hwang, P. P. Kuksa, B. D. Gregory, and 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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• 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, and 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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• 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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• 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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• 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, 2016.
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• 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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• Pavel P. Kuksa. Efficient multivariate sequence classification. In CoRR abs/1409.8211, 2013.
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• 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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• 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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• 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 P. Kuksa. Efficient time series classification with Multivariate similarity kernels. In NYAS Machine Learning Symposium, 2012.
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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 and Vladimir Pavlovic. Efficient evaluation of large sequence kernels. In KDD, 2012.
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• Pavel P. Kuksa. Using string kernels to predict gene expression. In Snowbird Learning Workshop, 2012.
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• Pavel P. Kuksa. 2D similarity kernels and representations for sequence data. In Snowbird Learning Workshop, 2012.
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• Pavel P. Kuksa, Imdadullah Khan, and Vladimir Pavlovic. Generalized Similarity Kernels for Efficient Sequence Classification. In SDM, 2012.
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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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• Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. Natural Language Processing (almost) from Scratch. Journal of Machine Learning Research, 12:2493–2537, 2011.
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• 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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• 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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• 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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• 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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• 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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• 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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• 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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• Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. High Performance Sequence Classification with Novel Spatial Sample Embedding. In NYAS Machine Learning Symposium, 2008.
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• Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Kernel Methods and Algorithms for General Sequence Analysis. Technical Report Rutgers University, 2008.
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• Pavel Kuksa, Pai-Hsi Huang, and Vladimir Pavlovic. Spatially-constrained sample kernel for sequence classification. In Snowbird Learning Workshop, 2008.
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• Pavel Kuksa and Vladimir Pavlovic. Efficient evaluation of large sequence kernels. In NYAS Machine Learning Symposium, 2011.
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• Pavel Kuksa and Vladimir Pavlovic. Efficient motif finding algorithms for large-alphabet inputs. BMC Bioinformatics, 11(Suppl 8):S1, 2010.
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• Pavel Kuksa and Vladimir Pavlovic. Efficient Sequence Classification with Spatial Representations. In Snowbird Learning Workshop, April 2010. Oral presentation [28/69].
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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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• 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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• 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 and Vladimir Pavlovic. Efficient Discovery of Common Patterns in Sequences. In Snowbird Learning Worskhop, 2009.
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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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• Pavel Kuksa and Vladimir Pavlovic. String kernel-based species identification using DNA barcodes. In Joint Molecular Biosciences Symposium, 2008.
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• 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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• Pavel Kuksa and Vladimir Pavlovic. Kernel methods for DNA barcoding. In Snowbird Learning Workshop, 2007.
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• 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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• Pavel Kuksa and Vladimir Pavlovic. Kernel Methods for DNA Barcoding. Technical Report Rutgers University, 2006.
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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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• Pavel P. Kuksa, Imdadullah Khan, and Vladimir Pavlovic. Generalized Similarity Kernels for Efficient Sequence Classification. Technical Report RU-DCS-TR684, Rutgers University, 2011.
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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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• 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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• 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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• 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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• 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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• 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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• 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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