Research Publications

Graduate research

I have worked on a range of phenomics and genomics projects:

Identifying epistatic mechanisms driving complex diseases
Multiomic data integration to study Alzheimer's disease using gene and pathway burden scores
GWAS/PRS of neuroimaging phenotypes to identify genetic predictors of schizophrenia, autism, and major depressive disorder
Implementing unsupervised clustering to subphenotype heart failure patient population using EHR and echo data
Elucidating comorbidity architecture of complex diseases using network medicine
Disease trajectory modeling using statistical modeling and machine learning

Research interest keywords: precision medicine, prognostics, EHR statistical genetics, machine learning, network science, time series, predictive modeling, patient-facing tool development

Publications

Singhal P, Kumar R, Morse C, Byrska-Bishop M, Ritchie MD, Verma A. {2023} Clinical event prediction of cardiovascular outcomes in EHR using disease trajectory modeling. Manuscript in preparation.

Singhal P, Tan A, Drivas T, Ritchie MD, Beaulieu-Jones B. Opportunities and Challenges for Biomarker Discovery Using Electronic Health Record Data. (2023) Manuscript submitted. Trends in Molecular Medicine.

Singhal P, Veturi S, et al. (2023) Largest genome-wide long range interaction study identifies interchromosomal associations across 5 complex diseases in the UK Biobank and eMERGE datasets. In press. American Journal for Human Genetics. Preprint: https://doi.org/10.1101/2022.10.19.22280888

Singhal P, Verma SS, Ritchie MD. (2023) Gene Interactions in Human Disease Studies – Evidence is Mounting. In press. Annual Review of Biomedical Data Science.

Singhal P, Guare L, Morse C, et al. (2023)DETECT: Feature extraction method for disease trajectory modeling. In press. American Medical Informatics Association Informatics Summit. Preprint: https://doi.org/10.1101/2022.11.06.22281817

Nam Y, Jung S, Yun J, Sriram V, Singhal P, et al. (2023) Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data. Bioinformatics. https://doi.org/10.1093/bioinformatics/btac822

Singhal P, Veturi Y, Judy R, Park Y, Vujkovic M, Veatch O, Kember R, Verma SS. (2023) session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction. Pacific Sym on Biocomputing. https://doi.org/10.1142/9789811270611_0037

Levin MG, Tsao NL, Singhal P, et al. (2022) Genome-wide association and multi-train analyses characterize the common genetic architecture of heart failure. Nature Communications. https://doi.org/10.1038/s41467-022-34216-6

Hwang G, Wen J, Sotardi S, Brodkin ES, Chand GB, Dwyer DB, Erus G, Doshi J, Singhal P, et al. (2022) Three Imaging Endophenotypes Characterize Neuroanatomical Heterogeneity of Autism Spectrum Disorder. JAMA Psychiatry. https://doi.org/10.1101/2022.06.17.22276543

Chand G, Singhal P, Dwyer D, et al. (2022) Schizophrenia imaging signatures and their associations with cognition, psychopathology, and genetics in the general population. American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.21070686

Reza N, Bone WP, Yang Y, Singhal P, et al. {2022} A supervised learning method for classification of electronic health-record based phenotypes. Manuscript in preparation.

Nguyen M, Singhal P, Monaghan JR, et al. (2017)Retinoic acid receptor regulation of epimorphic and homeostatic regeneration in the axolotl. Development. 144: 601-611. https://doi.org/10.1242/dev.139873

Friedland AE, Baral R, Singhal P, et al. (2015) Characterization of Staphylococcus aureus Cas9: a smaller Cas9 for all-in-one adeno-associated virus delivery and paired nickase applications. Genome Biology 16: 257. https://doi.org/10.1186/s13059-015-0817-8

Pankhuri Singhal

PhD Candidate @ University of Pennsylvania Dept of Genetics | Ritchie Lab

Publications

Pankhuri Singhal

PhD Candidate @ University of Pennsylvania Dept of Genetics | Ritchie Lab

Publications

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