Previous systems analyses in plants have focused on a single developmental stage or time point, although it is often important to additionally consider time-index changes. During seed development a cascade of events occurs within a relatively brief time scale. We have collected protein and transcript expression data from five sequential stages of Arabidopsis (Arabidopsis thaliana) seed development encompassing the period of reserve polymer accumulation. Protein expression profiling employed twodimensional gel electrophoresis coupled with tandem mass spectrometry, while transcript profiling used oligonucleotide microarrays. Analyses in biological triplicate yielded robust expression information for 523 proteins and 22,746 genes across the five developmental stages, and established 319 protein/transcript pairs for subsequent pattern analysis. General linear modeling was used to evaluate the protein/transcript expression patterns. Overall, application of this statistical assessment technique showed concurrence for a slight majority (56%) of expression pairs. Many specific examples of discordant protein/ transcript expression patterns were detected, suggesting that this approach will be useful in revealing examples of posttranscriptional regulation.

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Different automated decision support systems based on artificial neural network (ANN) have been widely proposed for the detection of heart disease in previous studies. However, most of these techniques focus on the preprocessing of features only. In this paper, we focus on both, i.e., refinement of features and elimination of the problems posed by the predictive model, i.e., the problems of underfitting and overfitting. By avoiding the model from overfitting and underfitting, it can show good performance on both the datasets, i.e., training data and testing data. Inappropriate network configuration and irrelevant features often result in overfitting the training data. To eliminate irrelevant features, we propose to use χ 2 statistical model while the optimally configured deep neural network (DNN) is searched by using exhaustive search strategy. The strength of the proposed hybrid model named χ 2-DNN is evaluated by comparing its performance with conventional ANN and DNN models, another state of the art machine learning models and previously reported methods for heart disease prediction. The proposed model achieves the prediction accuracy of 93.33%. The obtained results are promising compared to the previously reported methods. The findings of the study suggest that the proposed diagnostic system can be used by physicians to accurately predict heart disease. INDEX TERMS Deep neural network, heart disease, hyperparameters optimization, overfitting, underfitting.