David Knowles

Assistant Professor

Columbia University

United States

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David Knowles is an Assistant Professor at Columbia University in the United States. His research areas encompass computational biology, deep learning applications in genomics, and multi-omics data analysis, with a particular focus on diseases such as Alzheimer's and amyotrophic lateral sclerosis. His recent publications explore the use of functional annotations in mapping rare variants, the prediction of CRISPR guide RNA activity, and the impact of splicing aberrations in hematopoietic clonal outgrowths.

Articles (11)

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2025

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2021

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2021

Collaborators (6)

Neville Sanjana

New York University

UNITED STATES

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David Sulzer

Columbia University

UNITED STATES

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Gamze Gürsoy

Columbia University

UNITED STATES

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David knowles

University of Bristol

UNITED KINGDOM

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Brunilda Balliu

Assistant Professor

David Geffen School of Medicine at UCLA

UNITED STATES

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Tuuli Lappalainen

Professor at KTH Royal Institute of Technology

KTH Royal Institute of Technology

SWEDEN

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David Knowles

Research Interests

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Articles (11)

Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths.

Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning

Multiset correlation and factor analysis enables exploration of multi-omics data.

Computational models of dopamine release measured by fast scan cyclic voltammetry in vivo

LDmat: efficiently queryable compression of linkage disequilibrium matrices

Using epigenomics to understand cellular responses to environmental influences in diseases.

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes.

Deep mendelian randomization: Investigating the causal knowledge of genomic deep learning models

An integrated approach to identify environmental modulators of genetic risk factors for complex traits

Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization.

Collaborators (6)

Neville Sanjana

David Sulzer

Gamze Gürsoy

David knowles

Brunilda Balliu

Tuuli Lappalainen

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