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Results for multiple kernel function

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14 papers · ranked by Valyu relevance

01
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
02
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
03
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
04
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
05
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
06
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
07
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
08
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
09
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
10
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
11
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
12
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
13
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
14
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

Multiple-kernel learning for genomic data mining and prediction

Unsupervised multiple kernel learning for heterogeneous data integration

Multiple Kernel Learning approach for Medical Image Analysis

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

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

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

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

Multiple-kernel learning for genomic data mining and prediction

Unsupervised multiple kernel learning for heterogeneous data integration

Multiple Kernel Learning approach for Medical Image Analysis

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

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

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

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