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Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social...
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PrognosiT: Pathway/gene set-based tumour volume prediction using multiple kernel learning
BackgroundIdentification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for develo** new disease...
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Kernel-based testing for single-cell differential analysis
Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing...
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Improvement of variables interpretability in kernel PCA
BackgroundKernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data....
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Dynamics of neural fields with exponential temporal kernel
We consider the standard neural field equation with an exponential temporal kernel. We analyze the time-independent (static) and time-dependent...
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Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion
Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence...
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Multi-omics assists genomic prediction of maize yield with machine learning approaches
With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and...
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Hist2Vec: Kernel-Based Embeddings for Biological Sequence Classification
Biological sequence classification is vital in various fields, such as genomics and bioinformatics. The advancement and reduced cost of genomic...
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Using an Adaptive Neuro-fuzzy Interface System (ANFIS) to Estimate Walnut Kernel Quality and Percentage from the Morphological Features of Leaves and Nuts
Walnut genetic improvement and orchard management would significantly benefit from accurate prediction of critical yield-related traits. In this...
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Interpretable deep learning methods for multiview learning
BackgroundTechnological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics,...
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xCAPT5: protein–protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model
BackgroundPredicting protein–protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational...
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Traditional Kernel Regression
With non-normal outcome data, that remain non-normal in spite of transformations (Likert scales is a notorious example), data distributions may be...
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MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding
BackgroundIn many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification...
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Enhancing t-SNE Performance for Biological Sequencing Data Through Kernel Selection
The genetic code for many different proteins can be found in biological sequencing data, which offers vital insight into the genetic evolution of...
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Machine Learning Approach for Predicting Hydrothermal Liquefaction of Lignocellulosic Biomass
Hydrothermal liquefaction (HTL) of lignocellulosic biomass has gained attention as a promising technology for the production of biofuels and other...
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Kernel Ridge Regression (KRR)
Kernel regression is more sensitive than traditional ordinary least squares regression, but is a discretization model. By the add-up sum of...
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A novel multiple kernel fuzzy topic modeling technique for biomedical data
BackgroundText mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data...
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Kernelized multiview signed graph learning for single-cell RNA sequencing data
BackgroundCharacterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell...
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GKLOMLI: a link prediction model for inferring miRNA–lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm
BackgroundThe limited knowledge of miRNA–lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating...
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Reproducing Kernel Hilbert Spaces Regression and Classification Methods
The fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of...