Laboratory Corporation of America Summer Internship in San Diego, California

  1. Brief description of the intern's project:

Utilizing Deep Learning in Position Specific Modeling in Somatic Mutation Detection for Low Frequency Mutations

  1. How is this project meaningful to the students education:
  • Provides opportunity for the student to learn processing and analyzing large datasets from sequencing high-throughput instrument.

  • Student will learn the concepts of deep learning

  • Student gets an exposure to a practical use of dimensional reduction and feature selection in predictive modeling

  • This project provides an opportunity for the student to have hands experience on training machine learning methods using high dimensional data in an unbalance population

  1. How is this project valuable to the organization
  • Liquid Biopsy: Internal studies and peer reviewed publications have shown that technical and biological noise is loci dependent. Such behavior may be attributed to multitude of factors including sequencing context, evolutionary conservation aspects, epigenetics make up, process complexity, etc.. Learning and modeling the position specific noise in DNA/RNA sequencing is key in keeping the PPV under control for somatic mutation detection for a highly sensitive test, which shall benefit and array of products that involves NGS including Liquid Biopsy ( for cancer monitoring or companion diagnostics) , tissue profiling, or tumor burden estimation. Better PPV leads to less number of unnecessary orthogonal confirmations and manual curations which is conducive to an overall lower cost for the test. Hence making the test more accessible to community oncologists and more appealing to CROs.
  1. List project deliverables you will ask of an intern (what are their goals):
  • Survey of Deep learning tools available with pros and cons, Bringing one or more public tools in house.

  • Educate the team through one or more seminar about the deep learning concept with practical examples (use internal or publicly available datasets) for CNV detections, and modeling the noise during SNV detection.


Working on a Bachelor's Degree, and or professional certification.

Key competencies, skill sets and attributes needed for this scope of work (to be used in the job description):

  • Enrolled in a graduate program in computer science, applied math, bioinformatics, or related programs.

  • Course work and research activities in Machine Learning,

  • Course work and research activities in statistical modeling

  • Experience in programming in R and Python

  • Graduate level knowledge in algorithms

  • Familiarity with Linux operating system




Monday-Friday, 8-5

As an EOE/AA employer, the organization will not discriminate in its employment practices due to an applicant's race, color, religion, sex, national origin, sexual orientation, gender identity, disability or veteran status.