Kernel Multitask Regression for Toxicogenetics

Bernard, Elsa; Jiao, Yunlong; Scornet, Erwan; Stoven, Véronique; Walter, Thomas; Vert, Jean-Philippe

doi:10.1002/minf.201700053

Cited by 6 publications

(4 citation statements)

References 35 publications

(39 reference statements)

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“…We follow here a similar procedure with the difference that our task here is a regression task. A similar approach was carried out also in protein-ligand interaction prediction (Jacob and Vert, 2008) and in compound toxicity prediction (Bernard et al, 2017).…”

Section: Base Regressorsmentioning

confidence: 99%

Drug response prediction by ensemble learning and drug-induced gene expression signatures

Tan,

Özgül,

Bardak

et al. 2018

Preprint

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Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets of data provided an opportunity to apply machine learning methods to predict drug activity. However, due to the complexity of cancer drug mechanisms, none of the existing methods is perfect. In this paper, we propose a novel ensemble learning method to predict drug response. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate predictions by in vitro experiments in addition to the tests on data sets. The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/.

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Section: Base Regressorsmentioning

confidence: 99%

Drug response prediction by ensemble learning and drug-induced gene expression signatures

Tan,

Özgül,

Bardak

et al. 2018

Preprint

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“…Numerous ML-based techniques have been employed in these approaches ( Adam et al, 2020 ). Neural network models ( Xia et al, 2021 ) and Bayesian multitask multiple kernel learning ( Bernard et al, 2017 ; Manica et al, 2019 ) are some of these methods. Others are random forests (RF) ( Gayvert et al, 2017 ), support vector machines (SVMs) ( Huang et al, 2017 ), and naïve Bayes ( Kang et al, 2018 ; Anagaw & Chang, 2019 ; Patel et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Hajim,

Zainudin,

Mohd Daud

et al. 2024

PeerJ Computer Science

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Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL’s techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models’ generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

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“…Waegeman et al (2019) for overview). These include pairwise (kernel) learning (Ben-Hur and Noble 2005;Park and Chu 2009;Cichonska et al 2017Cichonska et al , 2018, dyadic prediction (Menon and Elkan 2010;Pahikkala et al 2014;Schäfer and Hüllermeier 2015), pair-input prediction (Park and Marcotte 2012), graph inference (Vert et al 2007), link prediction (Pieter and Koller 2005;Kashima et al 2009a), relational learning (Pahikkala et al 2010;Waegeman et al 2012;Pahikkala et al 2013), multi-task (Bonilla et al 2007;Bernard et al 2017) and as a special case zero-shot (Romera-Paredes and Torr 2015) learning.…”

Section: Introductionmentioning

confidence: 99%

Generalized vec trick for fast learning of pairwise kernel models

2022

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Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target or protein-protein interactions, or customer-product preferences. In this work, we present a comprehensive review of pairwise kernels, that have been proposed for incorporating prior knowledge about the relationship between the objects. Specifically, we consider the standard, symmetric and anti-symmetric Kronecker product kernels, metric-learning, Cartesian, ranking, as well as linear, polynomial and Gaussian kernels. Recently, a $$O(nm+nq)$$ O ( n m + n q ) time generalized vec trick algorithm, where $$n$$ n , $$m$$ m , and $$q$$ q denote the number of pairs, drugs and targets, was introduced for training kernel methods with the Kronecker product kernel. This was a significant improvement over previous $$O(n^2)$$ O ( n 2 ) training methods, since in most real-world applications $$m,q<< n$$ m , q < < n . In this work we show how all the reviewed kernels can be expressed as sums of Kronecker products, allowing the use of generalized vec trick for speeding up their computation. In the experiments, we demonstrate how the introduced approach allows scaling pairwise kernels to much larger data sets than previously feasible, and provide an extensive comparison of the kernels on a number of biological interaction prediction tasks.

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Kernel Multitask Regression for Toxicogenetics

Cited by 6 publications

References 35 publications

Drug response prediction by ensemble learning and drug-induced gene expression signatures

Drug response prediction by ensemble learning and drug-induced gene expression signatures

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Generalized vec trick for fast learning of pairwise kernel models

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