2016.01.13
2016年1月14日に久留米大学バイオ統計センター公開セミナーを開催します。
バイオ統計センター公開セミナー
日時：2016年1月14日（木）15:00-17:00
場所：バイオ統計センター コンピュータ室
講演者：西郷浩人（九州工業大学情報工学部生命情報工学科 准教授）
演題：「Mining Discriminative Patterns from Graph Data with
Multiple Labels and its Application to Quantitative
Structure-Relationship Analysis (QSAR)」
概要： Graph data are becoming increasingly common in machine
learning and data mining, and its application field pervades to
bioinformatics and cheminformatics. Accordingly, as a method
to extract patterns from graph data, graph mining recently has
been studied and developed rapidly. Since the number of patterns
in graph data is huge, a central issue is how to efficiently
collect informative patterns suitable for subsequent tasks such
as classification or regression. In this paper, we consider
mining discriminative subgraphs from graph data with multiple
labels. The resulting task has important applications in
cheminformatics, such as finding common functional groups that
trigger multiple drug side effects, or identifying ligand
functional groups that hit multiple targets. In computational
experiments, we first verify the effectiveness of the proposed
approach in synthetic data, then we apply it to drug adverse
effect prediction problem. In the latter dataset, we compared
the proposed method with L1-norm logistic regression in
combination with the PubChem/Open Babel fingerprint, in that the
proposed method showed superior performance with a much smaller
number of subgraph patterns.
Software is available from https://github.com/axot/GLP
http://pubs.acs.org/doi/10.1021/acs.jcim.5b00376
場所：バイオ統計センター コンピュータ室
講演者：西郷浩人（九州工業大学情報工学部生命情報工学科 准教授）
演題：「Mining Discriminative Patterns from Graph Data with
Multiple Labels and its Application to Quantitative
Structure-Relationship Analysis (QSAR)」
概要： Graph data are becoming increasingly common in machine
learning and data mining, and its application field pervades to
bioinformatics and cheminformatics. Accordingly, as a method
to extract patterns from graph data, graph mining recently has
been studied and developed rapidly. Since the number of patterns
in graph data is huge, a central issue is how to efficiently
collect informative patterns suitable for subsequent tasks such
as classification or regression. In this paper, we consider
mining discriminative subgraphs from graph data with multiple
labels. The resulting task has important applications in
cheminformatics, such as finding common functional groups that
trigger multiple drug side effects, or identifying ligand
functional groups that hit multiple targets. In computational
experiments, we first verify the effectiveness of the proposed
approach in synthetic data, then we apply it to drug adverse
effect prediction problem. In the latter dataset, we compared
the proposed method with L1-norm logistic regression in
combination with the PubChem/Open Babel fingerprint, in that the
proposed method showed superior performance with a much smaller
number of subgraph patterns.
Software is available from https://github.com/axot/GLP
http://pubs.acs.org/doi/10.1021/acs.jcim.5b00376
