| Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types G Wei, BJ Abraham, R Yagi, R Jothi, K Cui, S Sharma, L Narlikar, ... Immunity 35 (2), 299-311, 2011 | 383 | 2011 |
| Identifying regulatory elements in eukaryotic genomes L Narlikar, I Ovcharenko Briefings in functional genomics & proteomics 8 (4), 215-230, 2009 | 156 | 2009 |
| Genome-wide discovery of human heart enhancers L Narlikar, NJ Sakabe, AA Blanski, FE Arimura, JM Westlund, ... Genome research 20 (3), 381-392, 2010 | 154 | 2010 |
| A nucleosome-guided map of transcription factor binding sites in yeast L Narlikar, R Gordân, AJ Hartemink PLoS computational biology 3 (11), e215, 2007 | 129 | 2007 |
| Informative priors based on transcription factor structural class improve de novo motif discovery L Narlikar, R Gordân, U Ohler, AJ Hartemink Bioinformatics 22 (14), e384-e392, 2006 | 79 | 2006 |
| ChIP-Seq data analysis: identification of Protein–DNA binding sites with SISSRs peak-finder L Narlikar, R Jothi Next Generation Microarray Bioinformatics: Methods and Protocols, 305-322, 2012 | 62 | 2012 |
| Sequence features of DNA binding sites reveal structural class of associated transcription factor L Narlikar, AJ Hartemink Bioinformatics 22 (2), 157-163, 2006 | 60 | 2006 |
| Finding regulatory DNA motifs using alignment-free evolutionary conservation information R Gordaˆn, L Narlikar, AJ Hartemink Nucleic Acids Research 38 (6), e90-e90, 2010 | 55 | 2010 |
| Nucleosome Occupancy Information Improves de novo Motif Discovery L Narlikar, R Gordân, AJ Hartemink Annual International Conference On Research In Computational Molecular …, 2007 | 49 | 2007 |
| One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses L Narlikar, N Mehta, S Galande, M Arjunwadkar Nucleic acids research 41 (3), 1416-1424, 2013 | 33 | 2013 |
| A fast, alignment-free, conservation-based method for transcription factor binding site discovery R Gordân, L Narlikar, AJ Hartemink Annual International Conference on Research in Computational Molecular …, 2008 | 29 | 2008 |
| MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data L Narlikar Nucleic acids research 41 (1), 21-32, 2013 | 22 | 2013 |
| Multiple novel promoter-architectures revealed by decoding the hidden heterogeneity within the genome L Narlikar Nucleic acids research 42 (20), 12388-12403, 2014 | 19 | 2014 |
| CLARE: Cracking the LAnguage of Regulatory Elements L Taher, L Narlikar, I Ovcharenko Bioinformatics 28 (4), 581-583, 2012 | 16 | 2012 |
| Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes N Periyathambi, D Parkhi, Y Ghebremichael-Weldeselassie, V Patel, ... PloS one 17 (3), e0264648, 2022 | 14 | 2022 |
| DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP S Mitra, A Biswas, L Narlikar PLoS Comput Biol 14 (4), 2018 | 14 | 2018 |
| Identification and Computational Analysis of Gene Regulatory Elements L Taher, L Narlikar, I Ovcharenko Cold Spring Harbor Protocols 2015 (1), pdb. top083642, 2015 | 14 | 2015 |
| Orc4 spatiotemporally stabilizes centromeric chromatin L Sreekumar, K Kumari, K Guin, A Bakshi, N Varshney, BC Thimmappa, ... Genome Research 31 (4), 607-621, 2021 | 10 | 2021 |
| No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites S Mitra, L Narlikar Bioinformatics 32 (5), 779-781, 2016 | 9 | 2016 |
| Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus D Parkhi, N Periyathambi, Y Ghebremichael-Weldeselassie, V Patel, ... Iscience 26 (10), 2023 | 6* | 2023 |
