3 citations · 3 across the 5 of their papers we have counts for
5 papers
Iterative AI-guided optimisation of selective triple-drug combinations for breast cancer
Oghenejokpeme Orhobor, Abbi Abdel-Rehim, Emma Tate +5
Personalised cancer therapy aims to tailor treatment to individual tumour profiles, yet tumour heterogeneity and adaptive resistance continue to limit clinical efficacy. Drug combi…
Advancing Drug Development Through Strategic Cell Line and Compound Selection Using Drug Response Profiles
Abbi Abdel-Rehim, Emma Tate, Larisa N. Soldatova +1
Early identification of sensitive cancer cell lines is essential for accelerating biomarker discovery and elucidating drug mechanism of action. Given the efficiency and low cost of…
Genesis: Towards the Automation of Systems Biology Research
Ievgeniia A. Tiukova, Daniel Brunnsåker, Erik Y. Bjurström +8
The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for ye…
Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture
Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths +2
The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting…
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Ivan Olier, Noureddin Sadawi, G. Richard Bickerton +4
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance,…