Tero Aittokallio

TL;DR: The latest version of SynergyFinder 2.0 is described, which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements.

TL;DR: A novel reference model, named zero interaction potency (ZIP), is proposed, which captures the drug interaction relationships by comparing the change in the potency of the dose–response curves between individual drugs and their combinations, and utilizes a delta score to quantify the deviation from the expectation of zero interaction.

TL;DR: This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks and offers a personal view of the key future trends and developments in graph-based analysis of large-scale datasets.

TL;DR: A web application is implemented that uses key functions of R‐package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user‐friendly interface for visualizing the drug combination landscapes in an interactive manner.

TL;DR: An ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy could facilitate systematic drug repositioning of approved targeted drugs.