A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data

2010 ◽  
Vol 100 (2) ◽  
pp. 132-139 ◽  
Author(s):  
Klaus Jung ◽  
Marian Grade ◽  
Jochen Gaedcke ◽  
Peter Jo ◽  
Lennart Opitz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Therapy Response ◽  
Expression Data ◽  
Prediction Rules

scholarly journals A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency

Cell Cycle ◽  
2018 ◽  
Vol 17 (4) ◽  
pp. 486-491 ◽  
Author(s):  
Nicolas Borisov ◽  
Victor Tkachev ◽  
Maria Suntsova ◽  
Olga Kovalchuk ◽  
Alex Zhavoronkov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cancer Patients ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Data Transfer ◽  
Expression Data ◽  
Drug Efficiency

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

2014 ◽  
Vol 13 ◽  
pp. CIN.S19745 ◽  
Author(s):  
Leorey N. Saligan ◽  
Juan Luis Fernández-Martínez ◽  
Enrique J. deAndrés-Galiana ◽  
Stephen Sonis
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Fold Change ◽  
Learning Phase ◽  
Small Scale ◽  
Specific Gene ◽  
Expression Data ◽  
Validation Phase

Background Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). Methods A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase). Result The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy. Conclusion The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

On selection biases with prediction rules formed from gene expression data

2008 ◽  
Vol 138 (2) ◽  
pp. 374-386 ◽  
Author(s):  
J.X. Zhu ◽  
G.J. McLachlan ◽  
L. Ben-Tovim Jones ◽  
I.A. Wood
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Prediction Rules

scholarly journals eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13777 ◽  
Author(s):  
Zheng Chang ◽  
Zhenjia Wang ◽  
Cody Ashby ◽  
Chuan Zhou ◽  
Guojun Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Matrix Factorization ◽  
Expression Profiles ◽  
Nonnegative Matrix ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Cancer Subtypes ◽  
First Choice

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checkerboard patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

Comparison of LLE and PCA Algorithms for Gene Expression Data Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 378-381
Author(s):  
Xiao Zhou Chen ◽  
Fan Yang ◽  
Hua Mei Li ◽  
Jun Hua Chen
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Data Analysis ◽  
Dimensionality Reduction ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Linear Dimension ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Data Dimensionality Reduction

According to the problem that the linear dimension reduction is not effective to understand gene expression data. using the manifold learning as a guide, analysing dimensionality reduction of gene expression data, selecting colon cancer and leukaemia gene expression datasets for investigation, using inter category distances as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Experiments show that LLE algorithm is more suitable method for the gene expression data. The LLE analyses indicate that there is a clear distinction boundary between the healthy people and the cancer patients.

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