Thanneer Malai Perumal
Thanneer Malai Perumal
Digital Biomarker Technology Leader @ Roche
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Zitiert von
Zitiert von
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia
M Fromer, P Roussos, SK Sieberts, JS Johnson, DH Kavanagh, ...
bioRxiv 52209 (052209), 052209, 2016
Integrating pathways of Parkinson's disease in a molecular interaction map
KA Fujita, M Ostaszewski, Y Matsuoka, S Ghosh, E Glaab, C Trefois, ...
Molecular neurobiology 49 (1), 88-102, 2014
Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters
TE Sweeney, TD Azad, M Donato, WA Haynes, TM Perumal, R Henao, ...
Critical care medicine 46 (6), 915, 2018
A community approach to mortality prediction in sepsis via gene expression analysis
TE Sweeney, TM Perumal, R Henao, M Nichols, JA Howrylak, AM Choi, ...
Nature communications 9 (1), 1-10, 2018
Landscape of conditional eQTL in dorsolateral prefrontal cortex and co-localization with schizophrenia GWAS
A Dobbyn, LM Huckins, J Boocock, LG Sloofman, BS Glicksberg, ...
The American Journal of Human Genetics 102 (6), 1169-1184, 2018
Large-scale identification of common trait and disease variants affecting gene expression
ME Hauberg, W Zhang, C Giambartolomei, O Franzén, DL Morris, TJ Vyse, ...
The American Journal of Human Genetics 100 (6), 885-894, 2017
Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks
I Crespo, TM Perumal, W Jurkowski, A Del Sol
BMC systems biology 7 (1), 1-14, 2013
Gene Regulatory Network Inference of Immunoresponsive Gene 1 (IRG1) Identifies Interferon Regulatory Factor 1 (IRF1) as Its Transcriptional Regulator in Mammalian Macrophages.
MA Tallam A, Perumal TM, Antony PM, Jäger C, Fritz JV, Vallar L, Balling R ...
PLoS One 11 (2), e0149050, 2016
Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models
YW Wan, R Al-Ouran, CG Mangleburg, TM Perumal, TV Lee, K Allison, ...
Cell Reports 32 (2), 107908, 2020
Understanding dynamics using sensitivity analysis: caveat and solution
TM Perumal, R Gunawan
BMC systems biology 5 (1), 1-10, 2011
Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives
K Daily, SJH Sui, LM Schriml, PJ Dexheimer, N Salomonis, R Schroll, ...
Scientific data 4 (1), 1-10, 2017
Personalized hypothesis tests for detecting medication response in Parkinson disease patients using iPhone Sensor data
E CHAIBUB NETO, BM Bot, T PERUMAL, L Omberg, J Guinney, M Kellen, ...
Biocomputing 2016: Proceedings of the Pacific Symposium, 273-284, 2016
Detecting the impact of subject characteristics on machine learning-based diagnostic applications
EC Neto, A Pratap, TM Perumal, M Tummalacherla, P Snyder, BM Bot, ...
NPJ digital medicine 2 (1), 1-6, 2019
Detecting the impact of subject characteristics on machine learning-based diagnostic applications
EC Neto, A Pratap, TM Perumal, M Tummalacherla, P Snyder, BM Bot, ...
NPJ digital medicine 2 (1), 1-6, 2019
Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
SK Sieberts, TM Perumal, MM Carrasquillo, M Allen, JS Reddy, ...
Scientific Data 7 (1), 1-11, 2020
Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach
BA Logsdon, TM Perumal, V Swarup, M Wang, C Funk, C Gaiteri, M Allen, ...
bioRxiv, 510420, 2019
TRIM32 modulates pluripotency entry and exit by directly regulating Oct4 stability
TM Perumal, L Gonzalez-Cano, AL Hillje, L Taher, W Makalowski, ...
Scientific reports 5 (1), 1-19, 2015
Dynamical analysis of cellular networks based on the Green's function matrix
TM Perumal, Y Wu, R Gunawan
Journal of theoretical biology 261 (2), 248-259, 2009
A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health
E Chaibub Neto, A Pratap, TM Perumal, M Tummalacherla, BM Bot, ...
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study
EC Neto, TM Perumal, A Pratap, BM Bot, L Mangravite, L Omberg
arXiv preprint arXiv:1706.09574, 2017
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