Diagnosis of ulcerative colitis before onset of inflammation by multivariate modeling of genome-wide gene expression data

2009 ◽  
Vol 15 (7) ◽  
pp. 1032-1038 ◽  
Author(s):  
Jrgen Olsen ◽  
Thomas A. Gerds ◽  
Jakob B. Seidelin ◽  
Claudio Csillag ◽  
Jacob T. Bjerrum ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Multivariate Modeling ◽  
Genome Wide ◽  
Genome Wide Gene Expression

scholarly journals Genomic Machine Learning Model Predicts Radiation Therapy Benefit in Early-Stage Breast Cancer Patients with High Accuracy

2022 ◽  
Author(s):  
Kimberly Badal ◽  
Jerome E. Foster ◽  
Rajini Haraksingh ◽  
Melford John
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Early Stage ◽  
High Accuracy ◽  
Expression Data ◽  
Genome Wide ◽  
Number Of Genes ◽  
Genome Wide Gene Expression

Abstract BackgroundRadiation therapy (RT) is frequently recommended for post-surgery treatment of early-stage breast cancer (BC) patients, though not all benefit. Clinical factors currently guide RT treatment decisions. At present, models to predict RT-benefit predominantly use statistical methods with modest performance. In this paper we present a high-accuracy genomic Machine Learning (ML) model to predict RT-benefit in early-stage BC patients. We also present a novel method for selecting genomic features for training ML algorithms. MethodsGene expression data from 463 early-stage BC patients treated with surgery and RT from the METABRIC cohort were obtained. Wilcoxon Rank Sum (Wilcoxon RS) test and Cox Proportional Hazards (Cox PH) were used to reduce the number of genes used to train eight ML algorithms. ML algorithms were trained on 80% of data using 10-fold cross validation and tested on 20% of data to assess performance in predicting relapse status. Results Genome-wide gene expression data was reduced by 96% using Wilcoxon RS and Cox PH to a 1,596 gene set and a 977 gene set. These gene sets were used to train eight ML algorithms resulting in models that ranged in performance accuracies from 54.01% to 95.6%. Highest accuracies were obtained using Support Vector Machine (SVM977–93.41%, SVM1596–95.6%) and Neural Networks algorithms (NN977 – 92.31%, NN1596 – 93.41%). In RT-untreated patients, accuracies of all models were 30% to 40% lower compared to RT-treated patients. SVM977 had the highest sensitivity of 91.09%. Members of the 977 set were enriched with genes involved in cell cycle and differentiation as well as genes associated with radiosensitivity and radioresistance. Conclusion This study presents a novel genomic feature selection approach that used Wilcoxon RS followed by Cox PH to reduce the number of genes from genome-wide gene expression data used for training ML algorithms by 96%. This approach led to an SVM model that used the expression values of 977 genes to predict RT-benefit in early-stage BC patients with 93.41% accuracy. This work demonstrates that ML models can be clinically useful for predicting cancer patient outcomes.

scholarly journals pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (2) ◽  
pp. e32394 ◽  
Author(s):  
Morten Hansen ◽  
Thomas Alexander Gerds ◽  
Ole Haagen Nielsen ◽  
Jakob Benedict Seidelin ◽  
Jesper Thorvald Troelsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Principal Components ◽  
R Package ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Gene Expression

scholarly journals A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins

2020 ◽  
Vol 14 ◽  
Author(s):  
Mette Soerensen ◽  
Dominika Marzena Hozakowska-Roszkowska ◽  
Marianne Nygaard ◽  
Martin J. Larsen ◽  
Veit Schwämmle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cognitive Functioning ◽  
Association Study ◽  
Gene Expression Data ◽  
Monozygotic Twins ◽  
Later Life ◽  
Expression Data ◽  
Genome Wide ◽  
A Genome
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