Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data
| Autor: | Feiyu Xiong, Beilun Wang, Yanjun Qi, Moshe Kam, Leonid Hrebien |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Clustering high-dimensional data General Computer Science business.industry 02 engineering and technology High dimensional Machine learning computer.software_genre 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Expression data Kernelization Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Cancer gene Profiling (information science) 020201 artificial intelligence & image processing Data mining Artificial intelligence High dimensionality business computer Mathematics |
| Zdroj: | ACM Transactions on Knowledge Discovery from Data. 10:1-23 |
| ISSN: | 1556-472X 1556-4681 |
| DOI: | 10.1145/2789212 |
| Popis: | With the advancement of genome-wide monitoring technologies, molecular expression data have become widely used for diagnosing cancer through tumor or blood samples. When mining molecular signature data, the process of comparing samples through an adaptive distance function is fundamental but difficult, as such datasets are normally heterogeneous and high dimensional. In this article, we present kernelized information-theoretic metric learning (KITML) algorithms that optimize a distance function to tackle the cancer diagnosis problem and scale to high dimensionality. By learning a nonlinear transformation in the input space implicitly through kernelization, KITML permits efficient optimization, low storage, and improved learning of distance metric. We propose two novel applications of KITML for diagnosing cancer using high-dimensional molecular profiling data: (1) for sample-level cancer diagnosis, the learned metric is used to improve the performance of k -nearest neighbor classification; and (2) for estimating the severity level or stage of a group of samples, we propose a novel set-based ranking approach to extend KITML. For the sample-level cancer classification task, we have evaluated on 14 cancer gene microarray datasets and compared with eight other state-of-the-art approaches. The results show that our approach achieves the best overall performance for the task of molecular-expression-driven cancer sample diagnosis. For the group-level cancer stage estimation, we test the proposed set-KITML approach using three multi-stage cancer microarray datasets, and correctly estimated the stages of sample groups for all three studies. |
| Databáze: | OpenAIRE |
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