7,267 research outputs found
Predicting Pancreatic Cancer Using Support Vector Machine
This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that prediction accuracy, precision, recall with just genomic data is 80.77%, 20%, 4%. Prediction accuracy, precision, recall with just synthetic clinical data is 93.33%, 95%, 30%. While prediction accuracy, precision, recall for combination of real genomic and synthetic clinical data is 90.83%, 10%, 5%. The combination of real genomic and synthetic clinical data decreased the accuracy since the genomic data is weakly correlated. Thus we conclude that the combination of genomic and clinical data does not improve pancreatic cancer prediction accuracy. A dataset with more significant genomic features might help to predict pancreatic cancer more accurately
Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection
High-dimensional feature selection arises in many areas of modern science.
For example, in genomic research we want to find the genes that can be used to
separate tissues of different classes (e.g. cancer and normal) from tens of
thousands of genes that are active (expressed) in certain tissue cells. To this
end, we wish to fit regression and classification models with a large number of
features (also called variables, predictors). In the past decade, penalized
likelihood methods for fitting regression models based on hyper-LASSO
penalization have received increasing attention in the literature. However,
fully Bayesian methods that use Markov chain Monte Carlo (MCMC) are still in
lack of development in the literature. In this paper we introduce an MCMC
(fully Bayesian) method for learning severely multi-modal posteriors of
logistic regression models based on hyper-LASSO priors (non-convex penalties).
Our MCMC algorithm uses Hamiltonian Monte Carlo in a restricted Gibbs sampling
framework; we call our method Bayesian logistic regression with hyper-LASSO
(BLRHL) priors. We have used simulation studies and real data analysis to
demonstrate the superior performance of hyper-LASSO priors, and to investigate
the issues of choosing heaviness and scale of hyper-LASSO priors.Comment: 33 pages. arXiv admin note: substantial text overlap with
arXiv:1308.469
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