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Results for multiple kernel function

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16 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
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
03
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
04
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
05
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
06
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
07
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
08
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
09
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
10
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
11
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
12
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
13
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
14
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
15
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
16
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

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

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

Probabilistic Inference of Biological Networks via Data Integration

Random kernel k-nearest neighbors regression

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

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

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

Probabilistic Inference of Biological Networks via Data Integration

Random kernel k-nearest neighbors regression

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