Author

I. S. Haque

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 1 Genomics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:whole-genome sequencing· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Assay:imaging· 1

Frequent Co-Authors

G. Putcha· 2 shared T.-Y. Liu· 2 shared Y. Liu· 2 shared A. D. Schmitt· 1 shared A. Drake· 1 shared A. E. Carpenter· 1 shared A. Kannan· 1 shared A. S. Low· 1 shared

Activity by Year

2018-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersGenomics

G. Putcha

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

M. F. Cuccarese, B. A. Earnshaw, K. Heiser, B. Fogelson, C. T. Davis, P. F. McLean, H. B. Gordon, K.-R. Skelly, F. L. Weathersby, V. Rodic, I. K. Quigley, E. D. Pastuzyn, B. M. Mendivil, N. H. Lazar, C. A. Brooks, , C. C. Gibson

This paper develops a deep-learning enabled phenomics platform to identify immune-modulating compounds and applies it to study SARS-CoV-2 infection and cytokine storm, identifying clinically relevant compounds and new drug candidates.

bioRxiv · Aug 2, 2020 · PreprintImmunology

Spatial co-fragmentation pattern of cell-free DNA recapitulates in vivo chromatin organization and identifies tissues-of-origin

Y. Liu, T.-Y. Liu, D. E. Weinberg, B. W. White, C. J. De La Torre, C. L. Tan, A. D. Schmitt, S. Selvaraj, V. Tran, L. C. Laurent, L. Cabel, F.-C. Bidard, G. Putcha, I. S. Haque

The paper shows that fragment length patterns in cell-free DNA reflect in vivo chromatin organization and can identify tissues-of-origin.

bioRxiv · Oct 23, 2019 · PreprintGenomics

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

N. Wan, D. Weinberg, T.-y. Liu, K. Niehaus, D. Delubac, A. Kannan, B. White, E. Ariazi, M. Bailey, M. Bertin, N. Boley, D. Bowen, J. Cregg, A. Drake, R. Ennis, , I. S. Haque

This paper uses machine learning on whole-genome sequencing of plasma cell-free DNA to detect early-stage colorectal cancer with high sensitivity and specificity.

bioRxiv · Nov 24, 2018Cancer Biology