TY - CHAP
T1 - Disentangling disease heterogeneity with max-margin multiple hyperplane classifier
AU - Varol, Erdem
AU - Sotiras, Aristeidis
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - There is ample evidence for the heterogeneous nature of diseases. For example, Alzheimer’s Disease, Schizophrenia and Autism Spectrum Disorder are typical disease examples that are characterized by high clinical heterogeneity, and likely by heterogeneity in the underlying brain phenotypes. Parsing this heterogeneity as captured by neuroimaging studies is important both for better understanding of disease mechanisms, and for building subtype-specific classifiers. However, few existing methodologies tackle this problem in a principled machine learning framework. In this work, we developed a novel non-linear learning algorithm for integrated binary classification and subpopulation clustering. Non-linearity is introduced through the use of multiple linear hyperplanes that form a convex polytope that separates healthy controls from pathologic samples. Disease heterogeneity is disentangled by implicitly clustering pathologic samples through their association to single linear sub-classifiers. We show results of the proposed approach from an imaging study of Alzheimer’s Disease, which highlight the potential of the proposed approach to map disease heterogeneity in neuroimaging studies.
AB - There is ample evidence for the heterogeneous nature of diseases. For example, Alzheimer’s Disease, Schizophrenia and Autism Spectrum Disorder are typical disease examples that are characterized by high clinical heterogeneity, and likely by heterogeneity in the underlying brain phenotypes. Parsing this heterogeneity as captured by neuroimaging studies is important both for better understanding of disease mechanisms, and for building subtype-specific classifiers. However, few existing methodologies tackle this problem in a principled machine learning framework. In this work, we developed a novel non-linear learning algorithm for integrated binary classification and subpopulation clustering. Non-linearity is introduced through the use of multiple linear hyperplanes that form a convex polytope that separates healthy controls from pathologic samples. Disease heterogeneity is disentangled by implicitly clustering pathologic samples through their association to single linear sub-classifiers. We show results of the proposed approach from an imaging study of Alzheimer’s Disease, which highlight the potential of the proposed approach to map disease heterogeneity in neuroimaging studies.
UR - http://www.scopus.com/inward/record.url?scp=84947595306&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24553-9_86
DO - 10.1007/978-3-319-24553-9_86
M3 - Chapter
C2 - 28717788
AN - SCOPUS:84947595306
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 702
EP - 709
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -