Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

Saligan, Leorey N.; Fernández-Martínez, Juan Luis; deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

doi:10.4137/cin.s19745

Cited by 43 publications

(46 citation statements)

References 32 publications

(42 reference statements)

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“…For that purpose we have used the response criteria defined by Cheson et al (2007) [13]. has been successfully applied to predict risk of radiotherapy-related fatigue in prostate cancer patients using high dimensional expression data [14]. In this case, the challenge is not related to the dimension of the dataset, but to the heterogeneous degree of …”

Section: Methodsmentioning

confidence: 99%

On the prediction of Hodgkin lymphoma treatment response

et al. 2015

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-Martínez, jlfm@uniovi.es, 0034 985 103 199. AbstractPurpose: The cure rate in Hodgkin Lymphoma is high, but the response along the treatment is still unpredictable and is highly variable among patients. Detecting those patients that do not respond to the treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis, and designing a simple machine learning methodology to help physicians improving the treatment response assessment. Methods: We carried out a retrospective analysis of the response to treatment for a cohort of 263 Caucasians who were diagnosed with Hodgkin Lymphoma in Asturias (Spain). For that purpose, we used a list of 35 clinical and biological variables that are currently measured at diagnosis, before any treatment begins. To establish the list of most discriminatory prognostic variables for the treatment response we designed a machine learning approach based on two different feature selection methods (Fisher's ratio and Maximum Percentile Distance) and recursive feature elimination using a nearest-neighbor classifier (k-NN). The weights of the k-NN classifier are optimized using different terms of the confusion matrix (true and false positive rates) in order to minimize risk in the decisions. Results and conclusions: We found that the optimum strategy to predict treatment response in Hodgkin lymphoma consists in solving two

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Section: Methodsmentioning

confidence: 99%

On the prediction of Hodgkin lymphoma treatment response

et al. 2015

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“…The methodology tries to determine the shortest lists of most discriminatory genes that predict the NOP16 mutation and is described by Fernández-Martínez et al [20] and De Andrés-Galiana et al [22][23][24]. This classification problem is naturally unbalanced due to the low number of patients that show the NOP16 mutation, and the classifier has to take this feature into account.…”

Section: Methodsmentioning

confidence: 99%

“…We have used the Fisher's Ratio and Fold Change to rank the genes according to their discriminatory power [20,[22][23][24]. These gene-ranking methods and particularly Fisher's ratio turned to be very robust against different kind of noise [24].…”

Section: Methodsmentioning

confidence: 99%

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

Jl¹,

Ej²,

Cernea³

2017

J Mol Genet Med

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NOP16 was the third most important mutation (6.84%) in a reduced-size cohort of 117 patients with Chronic Lymphocytic Leukemia (CLL) whose most recurrent mutations were NOTCH1 (9.4%) and SF3B1 (8.55%). In this paper we analyzed the effect of the NOP16 mutation in gene expression. The NOP16 mutation was predicted with 100% accuracy using a small-scale signature formed by the 26 genes with the highest Fisher's Ratio. SLC39A4 (ZIP4) and WARS are the most discriminatory genes of this mutation providing a predictive accuracy of 97.4%. The Fold Change analysis also confirmed a very important role of the light (IGKV3D-11, IGKC and IGLJ3) and heavy (IGHG1) chain immunoglobulins, SOX11, CCND1 and CHL1. This analysis also highlights the importance of several mechanisms such as the ZIP4-related apoptosis, the SOX11-CCND1 over expression relationship observed in mantle cell-lymphoma, and CHL1 down regulation and over expression of the midkine-neurite growth-promoting factor that enhances the angiogenic and proliferative activities of cancer cells in different types of solid cancers. Besides, the holdout stability analysis has shown the importance of Signaling Events of B Cell Receptor (BCR), P53 signaling, Infectious disease, and TGF-beta Receptor Signaling. The integration of the NOP16 mutation with the IgHV, NOTCH1 and SF3B1 mutations, that were previously analyzed, confirmed that these mutations only share two high discriminatory genes: IGHG1 and RGS13. These genes are involved in different mechanisms concerning signaling and the immunological system. This analysis opens novel working hypothesis for CLL treatment and prognosis.

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“…The algorithm used in this article is similar to the one that was introduced in Saligan et al (2014) and(deAndrés-Galiana et al, 2015) and consists of several steps (see Fig. 1):…”

Section: Biomedical Robotsmentioning

confidence: 99%

“…techniques can help (de Andrés-Galiana et al, 2015, 2016 for instance in segmenting patients with respect to response to treatment (deAndrés-Galiana et al, 2015) and also to drug response, to predict the development of induced toxicities (Saligan et al, 2014), to infer the possible surgical risk, etc., among many different applications that we can imagine. Figure 1 shows a conceptual scheme of the biomedical robot concept.…”

Section: Biomedical Robotsmentioning

confidence: 99%

Design of Biomedical Robots for Phenotype Prediction Problems

deAndrés-Galiana

Fernández-Martínez

Sonis³

2016

Journal of Computational Biology

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Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and costeffectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

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Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

Cited by 43 publications

References 32 publications

On the prediction of Hodgkin lymphoma treatment response

On the prediction of Hodgkin lymphoma treatment response

The Effect of NOP16 Mutation in Chronic Lymphocytic Leukemia

Design of Biomedical Robots for Phenotype Prediction Problems

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