L. Narlikar

发表

One size does not fit all: On how Markov model order dictates performance of genomic sequence analyses

Sanjeev Galande, Leelavati Narlikar, Leelavati Narlikar, 2012, Nucleic acids research.

DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP

Leelavati Narlikar, Sneha Mitra, Anushua Biswas, 2017, bioRxiv.

Finding regulatory DNA motifs using alignment-free evolutionary conservation information

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2010, Nucleic acids research.

Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types.

Raja Jothi, Kairong Cui, K. Zhao, 2011, Immunity.

Genome-wide discovery of human heart enhancers.

John M Westlund, M. Nóbrega, I. Ovcharenko, 2010, Genome research.

Resolving diverse protein–DNA footprints from exonuclease-based ChIP experiments

L. Narlikar, Anushua Biswas, 2021, Bioinform..

ChIP-Seq data analysis: identification of protein-DNA binding sites with SISSRs peak-finder.

Raja Jothi, Leelavati Narlikar, L. Narlikar, 2012, Methods in molecular biology.

Identification and computational analysis of gene regulatory elements.

I. Ovcharenko, Leelavati Narlikar, L. Taher, 2015, Cold Spring Harbor protocols.

Identifying regulatory elements in eukaryotic genomes.

I. Ovcharenko, Leelavati Narlikar, L. Narlikar, 2009, Briefings in functional genomics & proteomics.

CLARE: Cracking the LAnguage of Regulatory Elements

Ivan Ovcharenko, Leelavati Narlikar, Leila Taher, 2012, Bioinform..

Multiple novel promoter-architectures revealed by decoding the hidden heterogeneity within the genome

Leelavati Narlikar, L. Narlikar, 2014, Nucleic acids research.

Towards a Complete Transcriptional Regulatory Code: Improved Motif Discovery Using Informative Priors

Leelavati Narlikar, L. Narlikar, 2008 .

MuMoD: a Bayesian approach to detect multiple modes of protein–DNA binding from genome-wide ChIP data

Leelavati Narlikar, Leelavati Narlikar, L. Narlikar, 2012, Nucleic acids research.

Orc4 spatiotemporally stabilizes centromeric chromatin

Leelavati Narlikar, R. Padinhateeri, R. Siddharthan, 2021, Genome research.

DNA replication initiator proteins facilitate CENPA loading on early replicating compact chromatin

A. Sanyal, R. Siddharthan, K. Sanyal, 2018, bioRxiv.

A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2007, PLoS Comput. Biol..

Nucleosome Occupancy Information Improves de novo Motif Discovery

Alexander J. Hartemink, Raluca Gordân, Leelavati Narlikar, 2007, RECOMB.

A universal framework for detecting cis-regulatory diversity in DNA regions

L. Narlikar, Anushua Biswas, 2021, Genome research.

A universal framework for detecting cis-regulatory diversity in DNA regulatory regions

L. Narlikar, Anushua Biswas, 2020, bioRxiv.

A predictive model of a growing fetus

G. I. Menon, U. Ram, R. Siddharthan, 2022, medRxiv.

MINDFUL: A Method to Identify Novel and Diverse Signals with Fast, Unsupervised Learning

Mallika Parulekar, L. Narlikar, 2019 .

No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites

Leelavati Narlikar, Sneha Mitra, Sneha Mitra, 2015, Bioinform..

Informative priors based on transcription factor structural class improve de novo motif discovery

Alexander J. Hartemink, Uwe Ohler, Raluca Gordân, 2006, ISMB.

Machine learning prediction of early postpartum prediabetes in women with gestational diabetes mellitus

R. Siddharthan, V. Patel, Saravanan Ponnusamy, 2023, medRxiv.

Ensemble of machine learning based prediction methods for diagnosis of schizophrenia using peripheral blood gene expression profile

Satyajeet P. Khare, S. Purohit, V. Paralikar, 2023, medRxiv.

Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes

R. Siddharthan, V. Patel, P. Saravanan, 2022, PloS one.

THiCweed: fast, sensitive detection of sequence features by clustering big datasets

Snehal V Sambare, R. Siddharthan, Ankit Agrawal, 2017, Nucleic acids research.

THiCweed: fast, sensitive detection of sequence features by clustering big data sets

Snehal V Sambare, R. Siddharthan, Ankit Agrawal, 2017, bioRxiv.

THiCweed: fast, sensitive motif finding by clustering big data sets

R. Siddharthan, Ankit Agrawal, L. Narlikar, 2017 .

Sequence features of DNA binding sites reveal structural class of associated transcription factor

Alexander J. Hartemink, Leelavati Narlikar, A. Hartemink, 2006, Bioinform..

Transcription factors organize into functional groups on the linear genome and in 3D chromatin

S. Hannenhalli, R. Siddharthan, Rakesh Netha Vadnala, 2022, bioRxiv.

CLARE: Cracking the LAnguage of Regulatory Elements

I. Ovcharenko, L. Taher, L. Narlikar, 2012, Bioinform..

ChIP-Seq data analysis: identification of protein-DNA binding sites with SISSRs peak-finder.

Raja Jothi, L. Narlikar, 2012, Methods in molecular biology.

Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus

P. Saravanan, N. Sukumar, Y. Weldeselassie, 2023, iScience.

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Satyajeet P. Khare, V. Paralikar, Tejaswini A. Pachpor, 2024, Neuropsychiatric disease and treatment.