Featured Research

Computational analysis of genomic and clinical data to aid medical decision making.

In the new "post-genome" era of personalized medicine, many variants critical to disease susceptibilities, prognosis and drug sensitivities will be identified and increased numbers of people will undergo DNA sequencing. We are developing algorithms and tools intended to facilitate this process.

Updates

  • Assistant Research Professor David Masica is applying his MOCA algorithm to classify cysts that may be precursors to pancreatic cancer. Read about how Dave's mathematical model is being used in Science Daily.
  • Read about this year's Critical Assessment of Genomes experiment CAGI4 and our team's win in a genotype to phenotype prediction challenge.

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Dr. Rachel Karchin

Associate Professor, The William R. Brody Faculty Scholar, Johns Hopkins University. Institute for Computational Medicine, Department of Biomedical Engineering, Department of Oncology.

About the Karchin Lab

We develop computational models to interpret and predict the impact of individual variation in the genome, transcriptome, and proteome. The models are being applied to cancer genomics, unclassified variants in Mendelian disease genes, and complex disease genetics. In collaboration with clinicians, pathologists, and experimental biologists, we aim to make significant improvements in individualized medicine within the next five years.

Lab Members and More Lab Info »

 

Featured Software Tool Tutorial

Selected Publications

Mascia DL, Karchin R (2016) PLoS Computational Biology. 12(5):e1004725. Article

Niknafs N, Guthrie VB, Naiman DQ, Karchin R (2015) PLoS Computational Biology 11(10):e1004416 Article

Newest Publication

Tokheim C Bhattacharya R, Niknafs N, Gygax DM, Kim R, Ryan M, Masica DL, Karchin R. May 2016 Article

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Latest Lab News

Summer 2016

David Masica to present webinar for Journal of American Medical Informatics Journal Club about "A novel approach for selecting combination clinical markers of pathology applied to a large retrospective cohort of surgically resected pancreatic cysts", Masica et al. JAMIA 2016. Thursday, August 11th, 2016, 3-4 ET. https://knowledge.amia.org/

"Towards Increasing the Clinical Relevance of In Silico Methods to Predict Pathogenic Missense Variants", Masica et al. PLoS Computational Biology, 2016 recommended by Faculty of 1000. Access the recommendation on F1000Prime.

Karchin Lab receives ITCR U24 grant from the NCI for Cancer-Related Analysis of VAriants Toolkit.

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