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Archives for March 2017

Random Forest

March 26, 2017 by wsygzyx Leave a Comment

Breiman, L. Machine Learning (2001) 45: 5. doi:10.1023/A:1010933404324


See relevant random-forest based paper in chemical biology field:

Data-Driven Approach to Drug Toxicity Prediction

Filed Under: Computer science

Ab initio or Empirical?

March 26, 2017 by wsygzyx Leave a Comment

With the development of modern super computers, ab initio method seems to be more feasible than previous applying to a large system, while empirical methods, can achieve more precise prediction to address problems in a specific project.

Filed Under: Chemistry

Systems Biology

March 24, 2017 by wsygzyx Leave a Comment

Two research papers published in 2002 by Hiroaki Kitano.

Kitano, H. (2002). Systems biology: a brief overview. Science (New York, N.Y.), 295(5560), 1662–4. https://doi.org/10.1126/science.1069492

Kitano, H. (2002). Computational systems biology. Nature, 420(6912), 206–210. https://doi.org/10.1038/nature01254


What is systems biology?

Filed Under: Chemistry

Data-Driven Approach to Drug Toxicity Prediction

March 23, 2017 by wsygzyx 1 Comment

A research article published in Cell Chemistry Biology in 2016

Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016). A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. Cell Chemical Biology, 23(10), 1294–1301. https://doi.org/10.1016/j.chembiol.2016.07.023


David C. Young mentioned in 2009 that the side effects of drugs are usually not identified until clinical trails which may results in drug failing clinical trails after already spending a large amount of money. [https://doi.org/10.1002/9780470451854] Failures in clinical trails have skyrocketed over the past three decades due to safety reasons. How to overcome this obstacle? The first thing comes to my mind is Big Data. Data-driven approaches have been used in almost all areas to solve different problems which is the reason I start to blog research articles of this cutting-edge area that interest me

In this specific case, Elemento et al. sought to use a similar “moneyball” approach, inspired by the effective use of sabermetrics in predicting successful baseball players (I don’t know baseball at all), to predict clinical toxicity, which is highlt related to successes and failures of clinical trials. This approach is called Predicting the Odds of Clinical Trial Outcomes Using Random Forest (PrOCTOR).图片1

This approach is shown in this figure (for detail illustration, check the video presented by the author: click here). It integrates chemical properties, drug-likeness measures, and target-based properties of a molecule into a random forest model to predict whether the drug is likely to be a member to fail clinical trials for toxicity reasons.

The set of 48 features taken into account in this research are listed in this file (click here).

Filed Under: Chemistry Tagged With: data, machine learning

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