Machine Learning in Cancer Immunotherapy

A small bioinformatics software company is trying to disrupt in precision cancer immunotherapy. They marked themselves using machine learning to predict the effect of a therapy.

Cancer cells

One of our consultant went in and took the role developing the backbone of the scientific platform implementing state-of-the-art machine learning applied on observational protein data scaling on the range of 10-20 GB. The algorithms include support vector machine, random forest, artificial neural network, and topic modeling. Some of these machine learning implementations are currently (as of May 2018) marked leading within their applications.

Consultants:

Alocias Mariadason

Alocias has a master's degree in physics from the University of Oslo, submitted in spring 2018. The subject of the thesis was Quantum Monte Carlo Simulations of Quantum dots constrained in single- and double well potentials. He solved the numerical problem for both systems with new analytic expressions which had not been explicitly done before and a newer method with roots in machine learning to further improve upon the results.

Jonathan Feinberg

Jonathan is a specialist within machine learning, stochastic modelling, statistics, and numerical programming. He has an eye for seeing possible solutions to the “hard” problems, and being able to quickly convert those into functioning prototypes.

Simen Tennøe

Simen is a computational scientist who finished his Ph.D. in computational neuroscience in 2019. He has a background from computational astrophysics where he wrote software for finding clusters of galaxies in cosmological N-body simulations. His Ph.D. focused on quantifying uncertainties in computational models of neurons and neural networks and he created a Python toolbox for performing these calculations.