Independent drug action in combination therapy: implications for precision oncology
Combination therapies are superior to monotherapy for many cancers. This advantage was
historically ascribed to the ability of combinations to address tumor heterogeneity, but …
historically ascribed to the ability of combinations to address tumor heterogeneity, but …
Drug combinations: a strategy to extend the life of antibiotics in the 21st century
Antimicrobial resistance threatens a resurgence of life-threatening bacterial infections and
the potential demise of many aspects of modern medicine. Despite intensive drug discovery …
the potential demise of many aspects of modern medicine. Despite intensive drug discovery …
Network-based prediction of drug combinations
Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an
important role in treating multiple complex diseases. Yet, our ability to identify and validate …
important role in treating multiple complex diseases. Yet, our ability to identify and validate …
Modeling polypharmacy side effects with graph convolutional networks
Motivation The use of drug combinations, termed polypharmacy, is common to treat patients
with complex diseases or co-existing conditions. However, a major consequence of …
with complex diseases or co-existing conditions. However, a major consequence of …
[HTML][HTML] Next-generation machine learning for biological networks
Machine learning, a collection of data-analytical techniques aimed at building predictive
models from multi-dimensional datasets, is becoming integral to modern biological research …
models from multi-dimensional datasets, is becoming integral to modern biological research …
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …
treatment, identifying novel synergistic combinations is challenging due to the size of …
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop
resistance that might be overcome with drug combinations. However, the number of possible …
resistance that might be overcome with drug combinations. However, the number of possible …
Molecular signatures of long-term hepatocellular carcinoma risk in nonalcoholic fatty liver disease
Prediction of hepatocellular carcinoma (HCC) risk is an urgent unmet need in patients with
nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from …
nonalcoholic fatty liver disease (NAFLD). In cohorts of 409 patients with NAFLD from …
Toward understanding and exploiting tumor heterogeneity
The extent of tumor heterogeneity is an emerging theme that researchers are only beginning
to understand. How genetic and epigenetic heterogeneity affects tumor evolution and …
to understand. How genetic and epigenetic heterogeneity affects tumor evolution and …