Download Machine Learning/Data Mining for Cancer Genomics

Machine Learning/Data Mining for Cancer Genomics
Bernard Manderick, Vrije Universiteit Brussel
Henry Nyongesa, University of the Western Cape
Collaboration:
Artificial Intelligence Laboratory – VUB
Intelligent Systems Laboratory - UWC
South Africa National Bioinformatics Institute - SANBI
Interuniversity Institute of Bioinformatics in Brussels - (IB)2
Project Outline
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Machine Learning (ML) is a rapidly growing field of research both in
terms of new techniques and applications.
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The most exciting aspect of ML is the No Free Lunch principle that
states no single ML-algorithm is optimal on types of problems, and
hence therefore you can’t have a priori knowledge if any one
technique is the most suitable for a particular problem.
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In this research we focus on using ML for “mining” large genomic
data sets in order to class human tumours.
Big Data and the Curse of Dimensionality
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The number of different types of datasets in the public domain
continues to grow exponentially.
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Academic computing research is currently addressing so called “big
data" solutions to make sense of the vast datasets coming out of
research in other disciplines, including genomics and bioinformatics.
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Such data sets are large scale and highly multi-dimensional and not
easily amenable to traditional data analysis tools.
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ML techniques are suited for automated knowledge discovery from large
complex data sets.
Data Mining and Cancer Genomics
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Data mining addresses the problem of discovering patterns, regularities
and structure within data collections.
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The field is for this reason, also referred to as “knowledge discovery
from databases” (KDD).
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Such discovered knowledge can then be applied to make predictions on
similar datasets, suggest explanation of dependencies between
independent variables, or generally improve decision making.
•
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Cancer is increasingly becoming more common in African populations.
Gene Expression profiling can be used to distinguish between known
cancer sub-types, and discover new types that may have remained
unknown to pathologists.
Research Collaboration between VUB and UWC
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Capacity building in competences for advanced research and
scholarship: VUB investigators will offer support, expertise and training
to UWC staff and students.
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Collaborative research into novel machine learning and data mining
techniques: Next Generation data mining techniques will require new
machine learning algorithms, and new methods for information storage
and retrieval, feature selection and selection optimization, and
optimization of decision making.
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International cooperation through staff and student exchange visits:
Make available and accessible to collaborating groups research and
educational material developed by either group.
Long Term Objectives
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Establishment of a Centre for Machine Learning and Data Mining
Applications at UWC.
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Human capacity development in the field of machine learning and
data mining.
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Recognition of South African science and technology through
international cooperation and collaboration.
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Dissemination of South African research output in international
workshops and conferences.
Significance of Research
This research aims to answer basic questions in cancer genomics
research:
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What is the most optimal and relevant representation of genomic
data?
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How to diagnose a patient based gene expression profile and on the
knowledge gained from previously assayed patients.
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How to best integrate genome-wide analytic tool into the large and
rapidly increasing amount of genome-wide datasets.
Mode of Collaboration
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Through staff and student exchange visits, and joint co-supervision of research
students.
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Participate in joint authorship, publication and dissemination of research
papers.
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Host a research conference/workshop in each year of the project, jointly
organised by the partners, at UWC during which identified international experts
and other field scientists shall be invited.
•
Make project decisions jointly in a democratic fashion, and with the maximum
amount of information available, after discussions at face-to- face meetings or by
email.
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Develop and establish an online collaboration platform within the project to
document data and code development, and other information related to the
project.
Work packages and Timelines
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WP1: Use Distributed/High Performance Computing for large sclae
genome analysis.
•
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WP2: Visits to enhance collaboration between partners
WP 3: Africa Workshops on Artificial Intelligence, Machine
Learning, Data Mining and Bioinformatics
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Establishment of UWC – Centre for Artificial Intelligence and Data
Mining.
Collaboration
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Professor Alan Christoffels, Director, SANBI, UWC.
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Professor Ann Nowe, VUB/(IB)2 (Machine
Learning/Bioinformatics)
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Professor Tom Lenaerts, VUB/(IB)2 (Bioinformatics)