1st International and 10th National Iranian Conference on Bioinformatics

Toxicoinfotrmatics: Recent Approaches in Integration of Big Data Biomath for the Benefit of healthcare policy

Paper ID : 1282-ICB10

Authors:

Parisa Shoae Hagh *

Abstract:

The era of in vivo, in vitro and ex vivo transmission to in silico experiments has been rapidly progressed in recent decades due to the benefits of computation in shortening the prediction time of results, reducing costs, and has provided significant advances in computational predictive toxicology, Including risk assessment of different organs toxicity at the encounter of high-risk molecules, estimating the co-effect of the time on life-long exposure, aggregating these effects with the consequences of multifactor underlying diseases and bringing forward system toxicology and ultimately using next generation sequencing results in designing individual-centered therapies for individual medicine [1-3]. However, existing data and massive calculations are still not being used as efficiently as they should be in achieving health goals [4,5]. In this article we are focused on introducing adverse outcome pathways coalition and outcome extrapolation strategies by artificial intelligence, integrated approaches of testing & prediction utility, some aspects of the most machine learning methods: support vector machines (SVMs), random forest (RF), decision trees (DTs), Naive Bayes, k-nearest neighbors (KNN) and neural networks and their current deficits in. finally propose practical solutions for current issues in big data analysis systems. We believe this is not a smooth road to endpoint application but a feasible prospect to beneficial decision-making regulators in favor of individuals one-by-one, in an example of what the world confrontes in the case of Covid-19, we summarize the unaccounted adverse outcomes of long-approved beneficial drugs because of the lack of massive calculations in the scale of whole population and how affected the general healthcare policies.

Keywords:

toxicoinformatics; big data; computational toxicology; system toxicology; machine learning

Status : Paper Accepted (Poster Presentation)