In pain, sleep and substance use disorders, we use bioinformatic and machine learning approaches to improve clinical phenotyping, as well as to understand their association with genetic variations. Our findings indicate genetic correlation between these disorders and highlight the importance of assessing and understanding the selective genetic variations associated with subtypes of these clinical groups in order to understand the multifactorial pathophysiology underlying these disorders and to identify potential treatment responders.

Sample Publications:

Guedj, R., Wang, H., Kossowsky, J., Fleegler, E., Berde, CB., Landschaft, A., Kimia, AA (2018). Cohort identification: when clinicians and artificial intelligence work hand in hand. Pediatric Academic Societies Meeting, Toronto, Canada.

Kossowsky, J. (2019). Migraine – spooky phenotypes and wild genotypes. 12th International Symposium on Pediatric Pain, Basel, Switzerland

Song W*, Kossowsky J*, Torous J, Chen CY, Huang H, Mukamal KJ, Berde CB, Bates DW, Wright A. Genome-wide association analysis of opioid use disorder: A novel approach using clinical data. Drug Alcohol Depend. 2020 Sep 15;217:108276. PubMed PMID: 32961455

Song W, Torous J, Kossowsky J, Chen CY, Huang H, Wright A. Genome-wide association analysis of insomnia using data from partners Biobank. Scientific Reports. 2020 Apr 24;10(1):1-8. PubMed Central PMCID: PMC7181749

* Co-First Authorship