In general, fully connected feature-encoding subnetworks outperformed other architectures.

Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. However, the space of possibilities is huge and efficient search strategies are required. A common strategy to overcome resistance is the use of combination therapies. In PLoS computational biology One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance.

In addition, some ordinary people who died in bizarre, freak, or strange circumstances are also included.Entries are correct to 31st December 2019.Pathology A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. It details the deaths of individuals from film, literature, music, theatre, and television, and the achievements they left behind, who died too early and often in tragic circumstances. Why did two friends of a well-known bass player steal his body then set it alight? How did the person who was found guilty of killing Kirsty MacColl manage to avoid going to jail? Which drummer of a famous group ended up homeless and destitute? Who froze to death in their car? Which famous guitarist disappeared as his group was about to start a tour of America and was never seen again? Which actress committed suicide by jumping from the top of the Hollywood sign? Whose pet monkey survived a plane crash? Find the answers to these intriguing questions and hundreds of more enthralling facts inside this book.First published in 2020 this book contains over 570 easily readable compact entries in systematic order augmented by an extensive bibliography, an alphabetical list of countries and locations of individuals final resting places (where known) and a day and month list in consecutive order of when an individual died.

Bizarre Deaths by BH & VHM McKechnie