miércoles, 23 de mayo de 2018

Predicting novel combination treatments for malaria using machine learning - BugBitten

Predicting novel combination treatments for malaria using machine learning - BugBitten

BugBitten: A blog for the 
parasites and vectors community

Predicting novel combination treatments for malaria using machine learning

recent paper by Bender et al in Malaria Journal discussed the authors' process and discoveries of using machine learning to formulate new combination therapies for malaria treatment. Below the authors share their research, and the wider implications of this study.


In 2016, the World Health Organization estimated there were 216 million cases of malaria worldwide. Among them, malaria caused death in approximately 445,000. The emergence of resistance to current antimalarial combination medicines (artemisinin-based combinations or ACT medicines) signifies the importance of identifying novel, anti-malarial drug combinations that can bypass current resistance mechanisms.

Dr Yasaman Kalantar Motamedi, Dr Rajarshi Guha & Dr Andreas Bender

Dr Yasaman Kalantar Motamedi is a PhD graduate of the University of Cambridge and a Bioinformatics research fellow at the University of Surrey specialized in Bioinformatics and Drug Discovery. Her work involved analysis of multi-omics data and utilizing bioinformatics as well as Machine learning techniques for making computational predictions for therapeutics and diagnosis purposes. She has validated the majority of her predictions successfully for different diseases with well-known international biotech institutes.

Dr. Rajarshi Guha was, until recently, a research scientist at the Division of Preclinical Innovation at the National Center for Advancing Translational Sciences (NCATS), within the National Institutes of Health (NIH). His research interests focus on methodology development to analyze and visualize chemical biology data sets, with a specific focus on techniques to link chemical structure information to molecular, bibliographic, genomic, and clinical covariances to explain the effects of small molecules in the context of larger biological systems.

Dr Andreas Bender is a Reader for Molecular Informatics at the Centre for Molecular Informatics at the Department of Chemistry at the University of Cambridge. His work is concerned with the analysis of chemical and biological data, to predict efficacy- or toxicity-related compound properties, as well as synergy effects of compound combinations.

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