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Daniel Parente, MD, PhD

Assistant Professor
Family Medicine and Community Health

PhD: University of Kansas Medical Center
MD: University of Kansas Medical Center
Residency, Family Medicine: University of Kansas Medical Center


Research Focus: Precision Medicine, Genomic Medicine, Preventive Care
Board Certification: American Board of Family Medicine
Clinical Responsibilities: Care of hospitalized patients
Non-clinical responsibilities: In-patient clinical informatics committee, Committee for Continuous Improvement Driven by Data (Quality Improvement)


Research Publications

Precision medicine and primary care

We are very interested in how preventive care is implemented and how advanced technologies like machine learning and pharmacogenomics can be used to improve patient outcomes.

Genomic medicine and rare disease

Multidisciplinary teams are needed to understand the clinical impact of novel or rare monogenic diseases. We have used exome sequencing to describe monogenic pathology in neurodevelopment.

  • 2019 - Sorting nexin 27 (SNX27) variants associated with seizures, developmental delay, behavioral disturbance, and subcortical brain abnormalities. DJ Parente, SM Morris, RC McKinstry, T Brandt, E Gabau, A Ruiz, and M Shinawi. Clin Genet. 2019 Nov 13 [PubMed] [Article (Wiley)]
  • 2016 - Neuroligin 2 nonsense variant associated with anxiety, autism, intellectual disability, hyperphagia, and obesity. DJ Parente, C Garriga, B Baskin, G Douglas, MT Cho, GC Araujo and M Shinawi. Am J Med Genet A 173(1):213-216 [PubMed] [Article (Wiley)]
Evolutionary architecture of proteins

Understanding the fundamental rules that constrain protein evolution is crucial for interpreting variants observed in patient genomes. Our interdisciplinary teams have used several model systems to interrogate these fundamental rules.

Software

  • PolyBoost- PolyBoost is a machine learning-based, post-analysis tool for the batch processing output of PolyPhen-2 that replaces the naive Bayes classifier with an extreme gradient boosting XGBoost classifier. Source code on Github or available through the Python Package Index (PyPI) as polyboost. The research publication describing this software is currently in peer review.
  • Co-evolution utilities - Protein co-evolutionary analysis utilities; relevant to our 2013 [PDF] and 2015 [PDF] papers on protein co-evolution. Source code on Github
  • MARS-Prot (Maintainer of Alignments using Reference Sequences for Proteins) - Maintenance tool for protein multiple sequence alignments (MSAs) that allows for the incorporating of new sequences into existing (hand-optimized) alignments. Source code on Github

Databases

  • AlloRep - A repository of sequence, structural and mutagenesis data for the LacI/GalR transcription repressor families. This is an important dataset for the development and validation of new bioinformatics analyses. Explore the database at allorep.org.

Collaborate

If you would like to collaborate on topics within precision medicine, genomic medicine, preventive care or understanding the evolutionary architecture of protein function, I would love to hear from you. Please contact me at dparente@kumc.edu.

Last modified: Dec 13, 2020

Contact

Daniel Parente, MD, PhD
Assistant Professor

dparente@kumc.edu

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