Publication

RANSAC-based Enhancement in Drug Concentration Prediction Using Support Vector Machine

Giovanni De Micheli, Alena Simalatsar, Wenqi You Dubout
2012
Conference Papers

Abstract

Training Support Vector Machines (SVMs) to predict drugs concentrations is often difficult because of the high level of noise in the training data, due to various kinds of measurement errors. We apply RANdom SAmple Consensus (RANSAC) algorithm in this paper to solve this problem, enhancing the prediction accuracy by more than 40% in our particular case study. A personalized sample selection method is proposed to further improve the prediction result in most cases.

Official source

https://infoscience.epfl.ch/entities/publication/3733fac4-7e16-48b5-9b25-b869970220eb

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Giovanni De Micheli, Alena Simalatsar, Wenqi You Dubout
2012
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3733fac4-7e16-48b5-9b25-b869970220eb

About this result

Related concepts (18)

Random sample consensus

Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed.

Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Gene prediction

In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced. In its earliest days, "gene finding" was based on painstaking experimentation on living cells and organisms.

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