| Natural language processing (almost) from scratch R Collobert, J Weston, L Bottou, M Karlen, K Kavukcuoglu, P Kuksa Journal of machine learning research 12, 2493− 2537, 2011 | 10331 | 2011 |
| New insights into the genetic etiology of Alzheimer’s disease and related dementias C Bellenguez, F Küçükali, IE Jansen, L Kleineidam, S Moreno-Grau, ... Nature genetics 54 (4), 412-436, 2022 | 1214 | 2022 |
| Systems and methods for semi-supervised relationship extraction Y Qi, B Bai, X Ning, P Kuksa US Patent 8,874,432, 2014 | 361 | 2014 |
| DASHR: database of small human noncoding RNAs YY Leung, PP Kuksa, A Amlie-Wolf, O Valladares, LH Ungar, S Kannan, ... Nucleic acids research 44 (D1), D216-D222, 2016 | 87 | 2016 |
| Efficient alignment-free DNA barcode analytics P Kuksa, V Pavlovic BMC bioinformatics 10, 1-18, 2009 | 81 | 2009 |
| New insights on the genetic etiology of Alzheimer’s and related dementia C Bellenguez, F Küçükali, I Jansen, V Andrade, S Moreno-Grau, N Amin, ... MedRxiv, 2020.10. 01.20200659, 2020 | 73 | 2020 |
| HIPPIE: a high-throughput identification pipeline for promoter interacting enhancer elements YC Hwang, CF Lin, O Valladares, J Malamon, PP Kuksa, Q Zheng, ... Bioinformatics 31 (8), 1290-1292, 2015 | 69 | 2015 |
| INFERNO: inferring the molecular mechanisms of noncoding genetic variants A Amlie-Wolf, M Tang, EE Mlynarski, PP Kuksa, O Valladares, Z Katanic, ... Nucleic acids research 46 (17), 8740-8753, 2018 | 59 | 2018 |
| DASHR 2.0: integrated database of human small non-coding RNA genes and mature products PP Kuksa, A Amlie-Wolf, Ž Katanić, O Valladares, LS Wang, YY Leung Bioinformatics 35 (6), 1033-1039, 2019 | 57 | 2019 |
| Scalable algorithms for string kernels with inexact matching P Kuksa, PH Huang, V Pavlovic Advances in neural information processing systems 21, 2008 | 54 | 2008 |
| Chemical modifications mark alternatively spliced and uncapped messenger RNAs in Arabidopsis LE Vandivier, R Campos, PP Kuksa, IM Silverman, LS Wang, BD Gregory The Plant Cell 27 (11), 3024-3037, 2015 | 46 | 2015 |
| High-order neural networks and kernel methods for peptide-MHC binding prediction PP Kuksa, MR Min, R Dugar, M Gerstein Bioinformatics 31 (22), 3600-3607, 2015 | 41 | 2015 |
| Efficient motif finding algorithms for large-alphabet inputs PP Kuksa, V Pavlovic BMC bioinformatics 11, 1-10, 2010 | 37 | 2010 |
| Generalized similarity kernels for efficient sequence classification P P. Kuksa, I Khan, V Pavlovic Proceedings of the 2012 SIAM International Conference on Data Mining, 873-882, 2012 | 34 | 2012 |
| A geometric approach to device-free motion localization using signal strength R Moore, R Howard, P Kuksa, RP Martin | 31 | 2010 |
| Semi-supervised sequence labeling with self-learned features Y Qi, P Kuksa, R Collobert, K Sadamasa, K Kavukcuoglu, J Weston 2009 Ninth IEEE International Conference on Data Mining, 428-437, 2009 | 30 | 2009 |
| Spatial representation for efficient sequence classification PP Kuksa, V Pavlovic 2010 20th International Conference on Pattern Recognition, 3320-3323, 2010 | 25 | 2010 |
| High-order semi-Restricted Boltzmann Machines and Deep Models for accurate peptide-MHC binding prediction R Min, P Kuksa, X Ning US Patent App. 14/512,332, 2015 | 24 | 2015 |
| Semi-supervised bio-named entity recognition with word-codebook learning PP Kuksa, Y Qi Proceedings of the 2010 SIAM International Conference on Data Mining, 25-36, 2010 | 23 | 2010 |
| Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer’s disease and highlights potential translational opportunities C Bellenguez, F Küçükali, I Jansen, V Andrade, S Morenau-Grau, N Amin, ... MedRxiv 17 (10), 2020 | 22 | 2020 |
Li-San WangUniversity of PennsylvaniaVerified email at upenn.edu
