Abstract 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 approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining. Results We provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches. Conclusion Multiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.

Mitja Briscik

Gabriele Tazza

László Vidács

Marie-Agnès Dillies

Sébastien Déjean

BioData Mining

BIODATA MINING 是一本开放获取、开放的同行评审期刊，涵盖应用于高维生物和生物医学数据的数据挖掘各个方面的研究，重点关注从大规模遗传、转录组、基因组、蛋白质组和代谢组学数据。主题领域包括但不限于：-新型数据挖掘和机器学习算法的开发、评估和应用。-传统数据挖掘和机器学习算法的适应、评估和应用。-开源软件用于数据挖掘和机器学习算法的应用。-数据库、软件和网络服务的设计、开发和集成，用于大规模研究数据的存储、管理、检索和分析。-预处理、后处理、数据挖掘和机器学习结果的建模和解释，用于生物解释和知识发现。

BioData Mining is an open-access, peer-reviewed journal that focuses on all aspects of data mining applied to high-dimensional biological and biomedical data. The journal covers topics including but not limited to: the development, evaluation, and application of novel data mining and machine learning algorithms; the adaptation, evaluation, and application of traditional data mining and machine learning algorithms; the application of open-source software for data mining and machine learning; the design, development, and integration of databases, software, and web services for the storage, management, retrieval, and analysis of large-scale research data; and the modeling and interpretation of preprocessing, postprocessing, data mining, and machine learning results for biological interpretation and knowledge discovery.

《BioData Mining》是一本开放获取的同行评审期刊，专注于高维生物和生物医学数据的数据挖掘研究。期刊涵盖的主题包括：新型数据挖掘和机器学习算法的开发、评估与应用；传统数据挖掘和机器学习算法的适应、评估与应用；开源软件在数据挖掘和机器学习中的应用；数据库、软件和网络服务的设计、开发与集成，以支持大规模研究数据的存储、管理、检索与分析；以及数据的预处理、后处理、挖掘和机器学习结果的建模与解释，以促进生物学解释和知识发现。

Category	Quartile
生物学	3
生物学, 数学与计算生物学	2

Supervised multiple kernel learning approaches for multi-omics data integration

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Journal Info

Category	Quartile
MATHEMATICAL & COMPUTATIONAL BIOLOGY	1