Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining machine
learning models. However, their estimation is complex from both theoretical and …
learning models. However, their estimation is complex from both theoretical and …
Machine learning methods, databases and tools for drug combination prediction
L Wu, Y Wen, D Leng, Q Zhang, C Dai… - Briefings in …, 2022 - academic.oup.com
Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …
reduce the development of drug resistance. However, even with high-throughput screens …
True to the model or true to the data?
A variety of recent papers discuss the application of Shapley values, a concept for
explaining coalitional games, for feature attribution in machine learning. However, the …
explaining coalitional games, for feature attribution in machine learning. However, the …
AttenSyn: an attention-based deep graph neural network for anticancer synergistic drug combination prediction
Identifying synergistic drug combinations is fundamentally important to treat a variety of
complex diseases while avoiding severe adverse drug–drug interactions. Although several …
complex diseases while avoiding severe adverse drug–drug interactions. Although several …
MatchMaker: a deep learning framework for drug synergy prediction
Drug combination therapies have been a viable strategy for the treatment of complex
diseases such as cancer due to increased efficacy and reduced side effects. However …
diseases such as cancer due to increased efficacy and reduced side effects. However …
[HTML][HTML] Machine learning for an explainable cost prediction of medical insurance
Predictive modeling in healthcare continues to be an active actuarial research topic as more
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
insurance companies aim to maximize the potential of Machine Learning (ML) approaches …
SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
Background In cancer research, high-throughput screening technologies produce large
amounts of multiomics data from different populations and cell types. However, analysis of …
amounts of multiomics data from different populations and cell types. However, analysis of …
Synergistic drug combination prediction by integrating multiomics data in deep learning models
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic
drug combinations could help to overcome drug resistance. However, the number of …
drug combinations could help to overcome drug resistance. However, the number of …
Enhancing scientific discoveries in molecular biology with deep generative models
Generative models provide a well‐established statistical framework for evaluating
uncertainty and deriving conclusions from large data sets especially in the presence of …
uncertainty and deriving conclusions from large data sets especially in the presence of …
A review of machine learning approaches for drug synergy prediction in cancer
A Torkamannia, Y Omidi… - Briefings in Bioinformatics, 2022 - academic.oup.com
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment
strategy for complex diseases such as malignancies. Identifying synergistic combinations …
strategy for complex diseases such as malignancies. Identifying synergistic combinations …