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Results for multiple kernel method

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8 papers · ranked by Valyu relevance

01
PPubMed23 Nov 2024
Supervised multiple kernel learning approaches for multi-omics data integration
Mitja Briscik, Gabriele Tazza, László Vidács, Marie-Agnès Dillies + 1 more
'Sébastien Déjean'] Background Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid…
89relevance
02
PPubMed14 Feb 2017
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
Kosuke Yoshida, Junichiro Yoshimoto, Kenji Doya
Background Advance in high-throughput technologies in genomics, transcriptomics, and metabolomics has created demand for bioinformatics tools to integrate high-dimensional data from different sources. Canonical correlation analysis (CCA) is a statistical tool for finding linear associations between different types of…
89relevance
03
PPubMed22 Aug 2017
Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation
Wenjia Niu, Kewen Xia, Baokai Zu, Jianchuan Bai
Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense…
88relevance
04
PPubMed22 Nov 2018
A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics
Longlong Liao, Kenli Li, Keqin Li, Canqun Yang + 1 more
Background While there are a large number of bioinformatics datasets for clustering, many of them are incomplete, i.e., missing attribute values in some data samples needed by clustering algorithms. A variety of clustering algorithms have been proposed in the past years, but they usually are limited to cluster on the…
87relevance
05
PPubMed15 Aug 2019
Multiple-kernel learning for genomic data mining and prediction
Christopher M. Wilson, Kaiqiao Li, Xiaoqing Yu, Pei-Fen Kuan + 1 more
'Xuefeng Wang'] Background Advances in medical technology have allowed for customized prognosis, diagnosis, and treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for the integration of multiple high throughput data sources. MKL remains to be…
86relevance
06
PPubMed16 Jun 2021
Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering
Meshal Shutaywi, Nezamoddin N. Kachouie, Antonio M. Scarfone
Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have been developed to improve the original k-means clustering method such…
82relevance
07
PPubMed8 Nov 2022
Feature-space selection with banded ridge regression
Tom Dupré la Tour, Michael Eickenberg, Anwar O. Nunez-Elizalde, Jack L. Gallant
'Jack L. Gallant'] Encoding models provide a powerful framework to identify the information represented in brain recordings. In this framework, a stimulus representation is expressed within a feature space and is used in a regularized linear regression to predict brain activity. To account for a potential…
81relevance
08
PPubMed6 Jun 2016
Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test
Esteban Vegas, Josep M. Oller, Ferran Reverter
Background Pathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two…
73relevance

Results for multiple kernel method

Supervised multiple kernel learning approaches for multi-omics data integration

Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics

Multiple-kernel learning for genomic data mining and prediction

Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering

Feature-space selection with banded ridge regression

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test

Supervised multiple kernel learning approaches for multi-omics data integration

Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics

Multiple-kernel learning for genomic data mining and prediction

Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering

Feature-space selection with banded ridge regression

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test