Authors

R Singh¹; C Louw¹; N Truter¹; Z Jansen van Rensburg¹; R Oudrhiri¹; D van Niekerk²; T Louw²; 
¹ Incubate.bio, UK;  ² Stellenbosch University, UK

Discussion

Experimental design in early drug discovery requires difficult prioritisations, based on constraints of cost and time. This can be addressed through the addition of causal inference analyses, which enrich classical ML/AI methods with detailed evidence about biological mechanisms.

These causal methods are used in several other industries already (such as Fintech and social media), but are yet to be ported to life sciences.  incubate.bio has taken up this challenge, and has over the last couple of years developed a computational biology platform called ALaSCA (Adaptable Large Scale Causal Analysis).  

incubate.bio is leveraging ALaSCA to both help life sciences companies with their experimental designs, and to build an internal programme for novel target discovery based on ageing processes in simple organisms for Alzheimer's Disease.

ALaSCA has been validated on molecular and clinical data.  In this work, we show recent results for an anti-microbial resistance (AMR) study - collaborative with Stellenbosch University (in Cape Town, SA) - where causal analysis shows how antibiotics in agriculture is the dominating factor for AMR in humans.