Search Results for author: Sebastian Nilsson

Found 5 papers, 4 papers with code

The Shapley Value in Machine Learning

2 code implementations11 Feb 2022 Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar

Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.

BIG-bench Machine Learning Data Valuation +5

ChemicalX: A Deep Learning Library for Drug Pair Scoring

1 code implementation10 Feb 2022 Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.

BIG-bench Machine Learning

Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

no code implementations20 Nov 2021 Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki, Eliseo Papa

For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent.

Drug Discovery Knowledge Graphs +2

A Unified View of Relational Deep Learning for Drug Pair Scoring

3 code implementations4 Nov 2021 Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, Eliseo Papa

In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed.

BIG-bench Machine Learning

MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy

2 code implementations28 Oct 2021 Benedek Rozemberczki, Anna Gogleva, Sebastian Nilsson, Gavin Edwards, Andriy Nikolov, Eliseo Papa

We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment.

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