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28 papers · ranked by Valyu relevance

01
PPubMed19 Jun 2017
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach
Yulin Jian, Daoyu Huang, Jia Yan, Kun Lu + 5 more
'Tanyue Zeng' 'Shijie Zhong' 'Qilong Xie'] A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose)…
86relevance
02
BbioRxiv13 Sep 2018
Multiple-kernel learning for genomic data mining and prediction
Christopher M. Wilson, Kaiqiao Li, Pei-Fen Kuan, Xuefeng Wang
Advances in medical technology have allowed for customized prognosis, diagnosis, and personalized treatment regimens that utilize multiple heterogeneous data sources. Multiple kernel learning (MKL) is well suited for integration of multiple high throughput data sources, however, there are currently no implementations…
86relevance
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…
86relevance
04
PPubMed2 Feb 2024
Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253
Haohan Xue, Ruixuan Zhang, Xudong Yan, Ruihan Wang + 1 more
PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative…
85relevance
05
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…
84relevance
06
BbioRxiv18 May 2017
Unsupervised multiple kernel learning for heterogeneous data integration
Jérôme Mariette, Nathalie Villa-Vialaneix
Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a…
84relevance
07
PPubMed13 Apr 2022
Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization
Wenbo Liu, Shengnan Liang, Xiwen Qin
The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this…
84relevance
08
AarXiv2021
Neural Generalization of Multiple Kernel Learning
Ahmad Navid Ghanizadeh, Kamaledin Ghiasi-Shirazi, Reza Monsefi, Mohammadreza Qaraei
'Mohammadreza Qaraei'] Multiple Kernel Learning is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep learning models and are inferior to these models in terms of recognition…
83relevance
09
BbioRxiv29 Mar 2017
Multiple Kernel Learning approach for Medical Image Analysis
Nisar Wani, Khalid Raza
Computer aided diagnosis is gradually making its way into the domain of medical research and clinical diagnosis. With field of radiology and diagnostic imaging producing petabytes of image data. Machine learning tools, particularly kernel based algorithms seem to be an obvious choice to process and analyze this high…
82relevance
10
AarXiv26 Mar 2024
MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation
Weiguo Gao
Length Extrapolation Authors: ['Weiguo Gao'] When the predicted sequence length exceeds the length seen during training, the transformer's inference accuracy diminishes. To address these challenges, the ability to generalize or extrapolate to longer sequence lengths has garnered increased attention. Existing relative…
81relevance
11
PPubMed22 Mar 2015
Probabilistic Inference of Biological Networks via Data Integration
Mark F. Rogers, Colin Campbell, Yiming Ying
There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring…
80relevance
12
AarXiv8 Jul 2017
Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples
Nora K. Speicher, Nico Pfeifer
Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene…
80relevance
13
BbioRxiv5 May 2020
Fenchel duality of Cox partial likelihood and its application in survival kernel learning
Christopher M. Wilson, Kaiqiao Li, Qiang Sun, Pei Fen Kuan + 1 more
The Cox proportional hazard model is the most widely used method in modeling time-to-event data in the health sciences. A common form of the loss function in machine learning for survival data is also mainly based on Cox partial likelihood function, due to its simplicity. However, the optimization problem becomes…
78relevance
14
AarXiv28 Nov 2018
Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating
Nicolas Langrené, Xavier Warin
Kernel density estimation and kernel regression are powerful but computationally expensive techniques: a direct evaluation of kernel density estimates at M evaluation points given N input sample points requires a quadratic O(MN) operations, which is prohibitive for large scale problems. For this reason, approximate…
77relevance
15
AarXiv2012
Multiple Operator-valued Kernel Learning
Hachem Kadri, Alain Rakotomamonjy, Francis Bach, Pierre‐Marie Preux
Positive definite operator-valued kernels generalize the well-known notion of reproducing kernels, and are naturally adapted to multi-output learning situations. This paper addresses the problem of learning a finite linear combination of infinite-dimensional operator-valued kernels which are suitable for extending…
75relevance
16
BbioRxiv20 Oct 2023
BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases
Sikta Das Adhikari, Yuehua Cui, Jianrong Wang
GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS…
73relevance
17
PPubMed1 Jul 2024
Random kernel k-nearest neighbors regression
Patchanok Srisuradetchai, Korn Suksrikran
The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big data contexts. However, this method is susceptible to overfitting and fit discontinuity, which present significant…
73relevance
18
BbioRxiv8 Feb 2025
An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels
Hyunwook Koh
In high-dimensional omics studies, researchers often conduct kernel association testing to power-fully detect the relationship of the genetic or microbial composition with human health or disease. Especially, in human microbiome studies, its dimension reduction analysis follows to visually represent complex microbiome…
72relevance
19
AarXiv2015
Operator-valued Kernels for Learning from Functional Response Data
Hachem Kadri, Emmanuel Duflos, Pierre‐Marie Preux, Stéphane Canu + 2 more
'Alain Rakotomamonjy' 'Julien Audiffren'] In this paper1 we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space…
72relevance
20
PPubMed1 Mar 2021
A guide for kernel generalized regression methods for genomic-enabled prediction
Abelardo Montesinos-López, Osval Antonio Montesinos-López, José Cricelio Montesinos-López, Carlos Alberto Flores-Cortes + 2 more
'José Cricelio Montesinos-López' 'Carlos Alberto Flores-Cortes' 'Roberto de la Rosa' 'José Crossa'] The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for…
69relevance
21
CchemRxiv28 Dec 2025
Accurate and Robust Analysis of Molecular Kinetics with Random Features
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
64relevance
22
CchemRxiv28 Aug 2025
An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction
Authors not listed
We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to define the similarity of molecular…
63relevance
23
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
61relevance
24
CchemRxiv21 Sep 2023
Machine Learning of Reactive Potentials
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, Olexandr Isayev + 1 more
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological and material sciences. The construction and training of MLPs enables fast and accurate simulations and analysis on thermodynamic and kinetic properties. This review focuses on the applications of…
59relevance
25
CchemRxiv25 Mar 2024
Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
59relevance
26
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
59relevance
27
CchemRxiv15 Jun 2022
Krein Support Vector Machine Classification of Antimicrobial Peptides
Joseph Redshaw, Darren Ting, Alex Brown, Jonathan Hirst + 1 more
Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and timeconsuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating…
58relevance
28
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
55relevance

Results for multiple kernel function

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

Multiple-kernel learning for genomic data mining and prediction

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253

Supervised multiple kernel learning approaches for multi-omics data integration

Unsupervised multiple kernel learning for heterogeneous data integration

Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization

Neural Generalization of Multiple Kernel Learning

Multiple Kernel Learning approach for Medical Image Analysis

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Probabilistic Inference of Biological Networks via Data Integration

Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples

Fenchel duality of Cox partial likelihood and its application in survival kernel learning

Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating

Multiple Operator-valued Kernel Learning

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases

Random kernel k-nearest neighbors regression

An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels

Operator-valued Kernels for Learning from Functional Response Data

A guide for kernel generalized regression methods for genomic-enabled prediction

Accurate and Robust Analysis of Molecular Kinetics with Random Features

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Krein Support Vector Machine Classification of Antimicrobial Peptides

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

Multiple-kernel learning for genomic data mining and prediction

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO Xue et al. 10.3389/fphar.2024.1257253

Supervised multiple kernel learning approaches for multi-omics data integration

Unsupervised multiple kernel learning for heterogeneous data integration

Weighted <i> p </i> -norm distance t kernel SVM classification algorithm based on improved polarization

Neural Generalization of Multiple Kernel Learning

Multiple Kernel Learning approach for Medical Image Analysis

MEP: Multiple Kernel Learning Enhancing Relative Positional Encoding Length Extrapolation

Probabilistic Inference of Biological Networks via Data Integration

Towards Multiple Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples

Fenchel duality of Cox partial likelihood and its application in survival kernel learning

Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating

Multiple Operator-valued Kernel Learning

BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases

Random kernel k-nearest neighbors regression

An ensemble learning method for joint kernel association testing and principal component analysis on multiple kernels

Operator-valued Kernels for Learning from Functional Response Data

A guide for kernel generalized regression methods for genomic-enabled prediction

Accurate and Robust Analysis of Molecular Kinetics with Random Features

An Adapted Similarity Kernel and Generalised Convex Hull for Molecular Crystal Structure Prediction

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Machine Learning of Reactive Potentials

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Krein Support Vector Machine Classification of Antimicrobial Peptides

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels