In order to investigate the breast cancer prediction problem on the aging population with the grades of DCIS, we conduct a tree augmented naive Bayesian network experiment trained and tested on a large clinical dataset including consecutive diagnostic mammography examinations, consequent biopsy outcomes and related cancer registry records in the population of women across all ages. Our tasks ar...

Mika et al. (in: Neural Network for Signal Processing, Vol. IX, IEEE Press, New York, 1999; pp. 41–48) apply the “kernel trick” to obtain a non-linear variant of Fisher’s linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark data sets. We show that leave-one-out cross-validation of kernel Fisher discriminant classi'ers can be implemented with a ...

Several methods (independent subsamples, leave-one-out, cross-validation, and bootstrapping) have been proposed for estimating the error rates of classiers. The rationale behind the various estimators and the causes of the sometimes con BLOCKINicting claims regarding their bias and precision are explored in this paper. The biases and variances of each of the estimators are examined empirically....

Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back prop...

MOTIVATION To understand transcription regulatory mechanisms, it is indispensable to investigate transcription factor (TF) DNA binding preferences. We noted that the generally acknowledged information of functional annotations of TFs as well as that of their target genes should provide useful hints in determining TF DNA binding preferences. RESULTS In this contribution, we developed an integr...

Recent literature provides many computational and modeling approaches for covariance matrices estimation in a penalized Gaussian graphical models but relatively little study has been carried out on the choice of the tuning parameter. This paper tries to fill this gap by focusing on the problem of shrinkage parameter selection when estimating sparse precision matrices using the penalized likelih...

This research aims to evaluate the performance of a Long Short-Term Memory (LSTM) based chatbot in answering questions (QnA). LSTM is type Recurrent Neural Network (RNN) architecture specifically designed overcome vanishing gradient problems and can store long-term information. The method used 5-fold cross-validation train model with 15 epochs at each fold using dataset provided. results showed...

The ability to predict structure from sequence is particularly important for toxins, virulence factors, allergens, cytokines, and other proteins of public heath importance. Many such functions are represented in the parallel /-helix fold class. Structure prediction for this fold is a challenging computational problem because there exists very little sequence similarity (less than 15%) across th...

The estimators most widely used to evaluate the prediction error of a non-linear regression model are examined. An extensive simulation approach allowed the comparison of the performance of these estimators for different non-parametric methods, and with varying signal-to-noise ratio and sample size. Estimators based on resampling methods such as Leave-one-out, parametric and non-parametric Boot...

We predict the ellipticity of galaxies from a variety of variables available through data from the Canada-France-Hawaii Lensing Survey. A survey of regression methods, including both nonparametric and tree-based methods, are applied to the prediction problem and results are discussed and compared. We present graphical methods for finding interactions between predictors. Leaveone-out cross-valid...