In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc.

No abstract

Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sucient supply of testing kits, hospital beds and medical personnel. This paper experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid- 19 pandemic cases. These ten methods include three types of neural networks and extrapola- tion methods based on best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method proposed by the second author. We also consider the Kriging and inverse distance weighting spatial interpolation methods. We also develop a novel spatiotemporal prediction method by combining temporal and spatial prediction methods. The experiments show that among these ten prediction methods, the spatiotemporal method has the smallest root mean square error and mean absolute error on Covid-19 cumulative data for counties in New York State between May and July, 2020.

No abstract