Manolis Kellis

About

Manolis Kellis is a Professor of Computer Science at MIT, specializing in Artificial Intelligence, Machine Learning, and their applications to healthcare and life sciences. His research areas include AI for Healthcare and Life Sciences, Biological and Medical Devices and Systems, and AI + Decision-making, combining traditions from computer science and electrical engineering to develop techniques for systems that interact with the external world through perception, communication, and action, while also learning, making decisions, and adapting to changing environments. His work involves leveraging computational, theoretical, and experimental tools to develop groundbreaking sensors, energy transducers, physical substrates for computation, and systems addressing shared human challenges. Kellis's research has contributed to understanding gene expression in Alzheimer’s disease, improving genetic prediction models across diverse populations, and transforming micronutrient dosing to improve child health in Nigeria.

Research topics

• Biology
• Genetics
• Computational biology
• Evolutionary biology
• Medicine
• Computer Science
• Internal medicine
• Cell biology
• Psychology
• Pathology

Selected publications

• The GTEx Consortium atlas of genetic regulatory effects across human tissues

  Science · 2020 · 5682 citations

  The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for alm…

  PublisherOA PDFDOI

• The repertoire of mutational signatures in human cancer

  Nature · 2020 · 3673 citations

  , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unk…

  DOI

• Pan-cancer analysis of whole genomes

  Nature · 2020 · 3248 citations

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  DOI

• Expanded encyclopaedias of DNA elements in the human and mouse genomes

  Nature · 2020 · 2538 citations

  data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the…

  DOI

• GENCODE 2021

  Nucleic Acids Research · 2020 · 1442 citations

  The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure…

  DOI

Recent grants

• NIH Grant U01HG007610

  NIH · $1.7M · 2018

• Network-based prediction and validation of causal schizophrenia genes and variants

  NIH · $2.1M · 2018–2021

• Single-cell transcriptional and epigenomic dissection of Alzheimer's Disease and Related Dementias

  NIH · $4.0M · 2018–2022

Frequent coauthors

• Irwin Jungreis

  Massachusetts Institute of Technology

  126 shared

• Li‐Huei Tsai

  96 shared

• Rory Johnson

  University Hospital of Bern

  81 shared

• Eloi Schmauch

  University of Eastern Finland

  76 shared

• Anshul Kundaje

  Stanford University

  75 shared

• Jason Ernst

  University of California, Los Angeles

  74 shared

• Mark Gerstein

  72 shared

• José Dávila-Velderrain

  70 shared

Labs

• MIT EECS - Manolis Kellis LabPI

Similar researchers at Massachusetts Institute of Technology

• Antonio Torralbah-133
• Daniela Rush-129
• Anantha Chandrakasanh-115