Scott Kulm

Graduate Trainee, Cornell University

3 active projects

Prostate Cancer

This workspace is designed to investigate the efficacy of genetic predictions for Prostate Cancer. Often times genetic predictions are more efficacious for people of European ancestry. We would like to see if genetic predictions generated from recently developed methods can…

Scientific Questions Being Studied

This workspace is designed to investigate the efficacy of genetic predictions for Prostate Cancer. Often times genetic predictions are more efficacious for people of European ancestry. We would like to see if genetic predictions generated from recently developed methods can break this trend and work equitably for people of all groups and backgrounds.

Project Purpose(s)

  • Disease Focused Research (prostate cancer)

Scientific Approaches

We will initially just survey the number of prostate cancer cases across different populations. If the sample sizes are sufficient we will generate polygenic risk scores, then lastly compare these scores against the case status within logistic regression models.

Anticipated Findings

If sample sizes are sufficient, we hypothesize that our polygenic risk scores, which are generated from newer, advanced methods, will perform with greater equability than former polygenic risk scores. This finding would contribute to the larger scientific field by definitely proving polygenic risk scores have the potential to work well across populations, and specifically which underlying method is the best to use.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Scott Kulm - Graduate Trainee, Cornell University

CRC and CBC

The goal of the project is to use machine learning algorithms to predict colorectal cancer risk by monitoring complete blood counts.

Scientific Questions Being Studied

The goal of the project is to use machine learning algorithms to predict colorectal cancer risk by monitoring complete blood counts.

Project Purpose(s)

  • Disease Focused Research (colorectal cancer)
  • Educational
  • Commercial

Scientific Approaches

We plan to use various machine learning algorithms from SciKit Learn to predict those with onset of colorectal cancer by monitoring complete blood counts.

Anticipated Findings

We hope to find that by monitoring complete blood counts that we will be able to improve screening for individuals with a higher predictive risk of developing colorectal cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

African American Prostate Cancer Polygenic Risk Score - WCM SPORE

build a multiethnic polygenic risk score for prostate cancer onset, and identify modifiable risk factors associated with the score. this is important for disease screening and public health

Scientific Questions Being Studied

build a multiethnic polygenic risk score for prostate cancer onset, and identify modifiable risk factors associated with the score. this is important for disease screening and public health

Project Purpose(s)

  • Disease Focused Research (prostate cancer)

Scientific Approaches

generate a polygenic risk score from all of us data and publicly available gwas summary statistics. regression models will be used to achieve these aims

Anticipated Findings

expect to a cross-ancestry risk model that can be deployed for preventative measures. existing models have poor cross-ancestry portability

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

  • Yajas Shah - Graduate Trainee, Cornell University
  • Scott Kulm - Graduate Trainee, Cornell University
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