Conor Moran

About Conor Moran

Conor is a graduate of Genetics from University College Dublin and an aspiring data scientist with First Derivatives. He is exploring the capabilities of machine learning within the life science disciplines where he is hoping to make a significant impact in years to come. Conor is currently working alongside RxDataScience to spearhead the efforts of changing the way we think about our genetic data from a clinical perspective.

Machine Learning for Pharma using Random Forest, Part II

Introduction to Machine Learning with Random Forest (Pharma/Genetics)


Deep Diving into the Genetics of Cancer – A Machine Learning Perspective, Part I

Machine Learning in Genomics & Cancer Treatment

When I first arrived in the RxDS (RxDataScience) Headquarters here in the Research Triangle Park as a Machine Learning researcher, I was informed that my first task would be to ‘Redefine Cancer Treatment’. As I’m sure you can understand, coming from a scientific background my curiosity levels were sky high. I was told that the key to bringing personalized medicine to cancer could be found within the genomes of cancer patients. Therein lies a multitude of mutations and variants, with some being benign passengers in this journey and others the core malignant drivers of the cancer itself (McFarland, Mirny and Korolev, 2014). Successfully interpreting these mutations and variants, using either traditional methods or contemporary data science solutions could lead to new treatments with each giving patients with the same underlying causes a fighting chance to overcome their individual cancers.