A Directed Acyclic Graphical Approach and Ensemble Feature Selection for a Better Drug Development Strategy Using Partial Knowledge from KEGG Signalling Pathways

Adel Aloraini

doi:10.1109/ICMLA.2014.98

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A Directed Acyclic Graphical Approach and Ensemble Feature Selection for a Better Drug Development Strategy Using Partial Knowledge from KEGG Signalling Pathways

Adel Aloraini

2014 13th International Conference on Machine Learning and Applications, pp.620-624

12/2014

DOI: https://doi.org/10.1109/ICMLA.2014.98

Abstract

Breast cancer

Databases

drug-design

Drugs

feature selection

Gene expression

Genomics

Graphical models

KEGG signalling pathways

Machine learning

Proteins

In this paper we consider the application of machine learning of graphical models and feature selection for developing better drug-design strategies. The work discussed in this paper is based on utilizing partial prior knowledge available through KEGG signalling pathway database in tan dim with our recent developed ensemble feature selection methods for a better regularisation of the lasso estimate. This work adds an extra layer of previously unseen knowledge in KEGG signalling pathways that embodies infering the underlying connectivity between gene-families implicated in breast cancer in MAPK-signalling pathway in response to application of anti-cancer drugs "neoadjuvant docetaxel".

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Details

Title: A Directed Acyclic Graphical Approach and Ensemble Feature Selection for a Better Drug Development Strategy Using Partial Knowledge from KEGG Signalling Pathways
Creators - without role: Adel Aloraini - Qassim University
Publication Details: 2014 13th International Conference on Machine Learning and Applications, pp.620-624
Publisher: IEEE
Identifiers: 9928794308331
Academic Unit: Qassim University
Language: English
Resource Type: Conference proceeding