Summary
I am a lecturer in cardiovascular bioinformatics and medical statistics at National Heart and Lung Institute, Imperial College London. My primary interest is in uncovering functional gene networks that underlie disease states and developing strategies for therapeutic interventions. I have a high level of experience in the fields of bioinformatics and computational biology and a strong track record in modern statistical approaches to the analysis and inference of biological networks and functional genomics.
Selected Publications
Journal Articles
Feleke R, Jazayeri D, Abouzeid M, et al., 2022, Integrative genomics reveals pathogenic mediator of valproate-induced neurodevelopmental disability, Brain, Vol:145, ISSN:0006-8950, Pages:3832-3842
Ji J, Anwar M, Petretto E, et al., 2022, PPMS: a framework to profile primary microRNAs from single-cell RNA-sequencing datasets, Briefings in Bioinformatics, Vol:23, ISSN:1467-5463, Pages:1-7
Feleke R, Reynolds RH, Smith AM, et al., 2021, Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases, Acta Neuropathologica, Vol:142, ISSN:0001-6322, Pages:449-474
Laaniste L, Srivastava P, Stylianou T, et al., 2019, Integrated systems-genetic analyses reveal a network target for delaying glioma progression, Annals of Clinical and Translational Neurology, Vol:6, ISSN:2328-9503, Pages:1616-1638
Srivastava P, van Eyll J, Godard P, et al., 2018, A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target, Nature Communications, Vol:9, ISSN:2041-1723
Srivastava PK, Bagnati M, Delahaye-Duriez A, et al., 2017, Genome-wide analysis of differential RNA editing in epilepsy, Genome Research, Vol:27, ISSN:1549-5469, Pages:440-450
Delahaye-Duriez A, Srivastava P, Shkura K, et al., 2016, Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery, Genome Biology, Vol:17, ISSN:1474-760X
Johnson MR, Shkura K, Langley SR, et al., 2016, Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease, Nature Neuroscience, Vol:19, ISSN:1546-1726, Pages:223-232
Rotival M, Ko J-H, Srivastava PK, et al., 2015, Integrating phosphoproteome and transcriptome reveals new determinants of macrophage multinucleation, Molecular & Cellular Proteomics, Vol:14, ISSN:1535-9476, Pages:484-498
Johnson MR, Behmoaras J, Bottolo L, et al., 2015, Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus, Nature Communications, Vol:6, ISSN:2041-1723
Srivastava PK, Moturu T, Pandey P, et al., 2014, A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction, BMC Genomics, Vol:15, ISSN:1471-2164, Pages:348-348
Srivastava P, Mangal M, Agarwal SM, 2014, Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set, Gene, Vol:535, ISSN:0378-1119, Pages:233-238
Srivastava PK, Hull RP, Behmoaras J, et al., 2013, JunD/AP1 regulatory network analysis during macrophage activation in a rat model of crescentic glomerulonephritis, BMC Systems Biology, Vol:7, ISSN:1752-0509
Hull RP, Srivastava PK, D'Souza Z, et al., 2013, Combined ChIP-Seq and transcriptome analysis identifies AP-1/JunD as a primary regulator of oxidative stress and IL-1 beta synthesis in macrophages, BMC Genomics, Vol:14, ISSN:1471-2164
Feuerborn A, Srivastava PK, Küffer S, et al., 2011, The Forkhead factor FoxQ1 influences epithelial differentiation., J Cell Physiol, Vol:226, Pages:710-719
Srivastava PK, Küffer S, Brors B, et al., 2008, A cut-off based approach for gene expression analysis of formalin-fixed and paraffin-embedded tissue samples., Genomics, Vol:91, Pages:522-529
Srivastava PK, Desai DK, Nandi S, et al., 2007, HMM-ModE--improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying emission probabilities with negative training sequences., Bmc Bioinformatics, Vol:8