Prediction of essential genes in a life organism is one of the central tasks in synthetic biology. Computational predictors are desired because experimental data is often unavailable. Recently, some sequence-based predictors have been constructed to identify essential genes. However, their predictive performance should be further improved. One key problem is how to effectively extract the sequence-based features, which are able to discriminate the essential genes. Another problem is the imbalanced training set. The amount of essential genes in human cell lines is lower than that of non-essential genes. Therefore, predictors trained with such imbalanced training set tend to identify an unseen sequence as a non-essential gene. Here, a new over-sampling strategy was proposed called Clustering based Synthetic Minority Oversampling Technique (CSMOTE) to overcome the imbalanced data issue. Combining CSMOTE with the Z curve, the global features, and Support Vector Machines, a new protocol called iEsGene-CSMOTE was proposed to identify essential genes. The rigorous jackknife cross validation results indicated that iEsGene-CSMOTE is better than the other competing methods. The proposed method outperformed λ-interval Z curve by 35.48% and 11.25% in terms of Sn and BACC, respectively.

Image reconstruction is an important research direction in computer vision. In this paper, a deep sparse representation model is proposed for super-resolution image reconstruction. We firstly study the decomposition of sparse coefficients and the construction of over-complete dictionary, and then use the K- VSD algorithm to extract the image sparse feature. Finally the deep feature migration model is designed to refine image features with deep convolutional neural network (CNN). The experiments carry out on the perspective single-channel, multi-channel and pixel-wise amplitude reconstruction. Both subjective assessments and objective metrics demonstrate that the proposed method has a good reconstruction effect.