Label distribution learning (LDL) can more accurately represent the degree of correlation between labels and samples than multi-label learning. However, LDL usually has limited available label data, which is not conducive to training deep learning models. Data augmentation refers to methods for solving limited data problems by introducing unobserved data or latent variables to increase the size and quality of the training dataset. In this paper, we augment the dataset by mapping the features and label distributions of the generated samples to the same subspace, and using the generator to learn the distribution of the original data in this space. First, we use the encoder and generator to extract effective information from sample features and label distributions, respectively. Second, we randomly fuse the existing label distribution to generate a new label distribution, then map it to the subspace and restore the corresponding features through the decoder. Finally, these generated samples are mixed with the original training set to train a model for predicting label distribution. Experimental results on nine real-world datasets show that our proposed algorithm can improve the performance of deep learning models to a certain extent.

Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics.