Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). 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.

Definitely not for the squeamish or sensitive.Pathology A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. It does cover quite a fascinating area by detailing individuals from the world of film, literature, music, sport, theatre, and television, as well as many ordinary people, all of whom died in bizarre, extraordinary, peculiar, strange, or unusual circumstances.Ĭomplimentary | The entries are concise but get straight to the point while giving all the relevant information without the padding you can find in some other publications dealing with this subject.Ĭritical | Like the larger publication this is also a very harrowing book, which makes uncomfortable reading at times. Bizarre Deaths | Famous Individuals and Others Who Died In Horrific Circumstancesįirst published in 2021 this is an abridged version of their major publication Untimely and Tragic Deaths of the Renowned The Celebrated The Iconic | Featuring Ordinary Individuals Who Died In Bizarre Circumstances.

Bizarre Deaths by BH & VHM McKechnie