Random forest dissimilarity based multi-view learning for Radiomics application

Cao, Hongliu; Bernard, Simon; Sabourin, Robert; Heutte, Laurent

doi:10.1016/j.patcog.2018.11.011

Cited by 59 publications

(48 citation statements)

References 43 publications

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“…Prior to kernel construction, the forests of ∆ * are re-grown with 2000 trees each. Higher number of trees and smaller leaf size tend to provide better approximation to the class discrimination boundary, as demonstrated in [10].…”

Section: Aklimate Hyperparametersmentioning

confidence: 94%

Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge

Uzunangelov

Wong

Stuart

2020

Preprint

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Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, several databases have amassed information about pathways and gene "signatures" -- patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE outperforms state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality.

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Section: Aklimate Hyperparametersmentioning

confidence: 94%

Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge

Uzunangelov

Wong

Stuart

2020

Preprint

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“…is still equal to 0, but in the opposite situation the resulting value is in ]0, 1]. A second variant [5], noted RFD in the following, leans on a measure of instance hardness, namely the κ-Disagreeing Neighbors (κDN) measure [21], that estimates the intrinsic difficulty to predict an instance as follows:…”

Section: Using Random Forest For Measuring Dissimilaritiesmentioning

confidence: 99%

“…Any of these variants could be used to compute the dissimilarities in our framework. However, we choose to use the RFD variant in the following, since it has been shown to give very good results when used for building dissimilarity representations for multi-view learning [5].…”

Section: Using Random Forest For Measuring Dissimilaritiesmentioning

confidence: 99%

“…It can be proven that the matrices derived from the initial RF proximity measure [2,23] are p.s.d and can be used as pre-computed kernels in SVM classifiers [6], following the embedding approach. However, the proof does not apply if the matrices are obtained using the RFD measure [5]. This is the main reason we use the dissimilarity space strategy in this work, as it allows more flexibility.…”

Section: The Dissimilarity Spacementioning

confidence: 99%

“…It may also be due to the use of several feature extractors, such as in image recognition tasks, often based on multiple representations of features, such as color, shape, texture descriptors, etc. [6] Learning from these types of data is called multi-view learning and each modality/set of features is called a view. For this type of task, it is assumed that the views convey different types of information, each of which can contribute to the pattern recognition task.…”

Section: Introductionmentioning

confidence: 99%

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Random Forest for Dissimilarity-Based Multi-View Learning

Bernard

Cao

Sabourin

2020

Handbook of Pattern Recognition and Computer Vision

Self Cite

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Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-ofthe-art static view combinations.

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Statistical Similarity in Machine Learning

Klyushin

2023

Machine Learning Applications

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Random forest dissimilarity based multi-view learning for Radiomics application

Cited by 59 publications

References 43 publications

Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge

Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge

Random Forest for Dissimilarity-Based Multi-View Learning

Statistical Similarity in Machine Learning

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