Expression Feature

Abstract: Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.

Abstract: Aiming at the disadvantages of the traditional machine-based facial expression recognition method that eliminates the feature of manual selection, a feature extraction method based on deep convolutional neural network to learn expression features is proposed. Since the deep convolutional neural network can directly use the original image as the input image, the image abstract feature interpretation is obtained at the fully connected layer of the image, which avoids the inherent error of image preprocessing and artificial selection features. Then, we reconstruct the traditional local binary pattern (LBP) feature operator for facial expression image and fuse the abstract facial expression features learned by the deep convolution neural network with the modified LBP facial expression texture features in the full connection layer. A new facial expression feature can be obtained, and the classification accuracy can be improved. In general, for the recognition of facial expression images, the proposed method based on the fusion LBP expression features and convolutional neural network expression features is used to obtain the best performance of 91.28% in the comparative experiment. An efficient extension of the expression feature texture expression channel is carried out. On the other hand, convolutional neural networks have incomparable advantages over other methods in abstract information representation of two-dimensional images.