Kathleen Hill

Associate Professor

University of Western Ontario

Canada

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About

Kathleen Hill is an Associate Professor at The University of Western Ontario, Canada. Her research focuses on cancer biology, machine learning applications in virology, and the interactions between mitochondrial and nuclear genomes. Recent publications explore topics such as genomic surveillance of SARS-CoV-2, the role of non-coding RNAs, and the ecological impacts on prokaryotic extremophiles.

Articles (13)

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2024

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2023

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2023

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2022

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2022

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2022

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2020

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2020

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2020

Collaborators (3)

Christina Castellani

Western University

CANADA

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Camila P. E. de Souza

Assistant Professor

University of Western Ontario

CANADA

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Eva Turley

Professor

University of Western Ontario

CANADA

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Kathleen Hill

Research Interests

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About

Articles (13)

CD44 and RHAMM Are Microenvironmental Sensors with Dual Metastasis Promoter and Suppressor Functions.

Leveraging machine learning for taxonomic classification of emerging astroviruses

Decoding Mitochondrial–Nuclear (epi)genome Interactions: The Emerging Role of ncRNAs

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

iDeLUCS: a deep learning interactive tool for alignment-free clustering of DNA sequences

SomaticSiMu: A mutational signature simulator

Colonization of <i>Brachypodium distachyon</i> by <i>Gluconacetobacter diazotrophicus</i> and its effect on plant growth promotion

DeLUCS: Deep learning for unsupervised clustering of DNA sequences

Mutational Patterns Observed in SARS-CoV-2 Genomes Sampled From Successive Epochs Delimited by Major Public Health Events in Ontario, Canada: Genomic Surveillance Study

Differential Gene Expression of Brachypodium distachyon Roots Colonized by Gluconacetobacter diazotrophicus and the Role of BdCESA8 in the Colonization

Postzygotic Somatic Mutations in the Human Brain Expand the Threshold-Liability Model of Schizophrenia

MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Collaborators (3)

Christina Castellani

Camila P. E. de Souza

Eva Turley

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