Repository: Freie Universit├Ąt Berlin, Math Department

Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining

Jayrannejad, Fahrnaz and Conrad, T. O. F. (2017) Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining. Springer Lecture Notes in Artificial Intelligence .

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Official URL: http://www.springer.com/gp/computer-science/lncs

Abstract

Recent advances in -omics technology has yielded in large data-sets in many areas of biology, such as mass spectrometry based proteomics. However, analyzing this data is still a challenging task mainly due to the very high dimensionality and high noise content of the data. One of the main objectives of the analysis is the identification of relevant patterns (or features) which can be used for classification of new samples to healthy or diseased. So, a method is required to find easily interpretable models from this data. To gain the above mentioned goal, we have adapted the disjunctive association rule mining algorithm, TitanicOR, to identify emerging patterns from our mass spectrometry proteomics data-sets. Comparison to five state-of-the-art methods shows that our method is advantageous them in terms of identifying the inter-dependency between the features and the TP-rate and precision of the features selected. We further demonstrate the applicability of our algorithm to one previously published clinical data-set.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics > Mathematical Modelling
Mathematical and Computer Sciences > Artificial Intelligence > Machine Learning
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group
ID Code:2094
Deposited By: Admin Administrator
Deposited On:08 Aug 2017 07:00
Last Modified:19 Mar 2018 09:54

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