Search

ABSTRACTRecent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiensN6-methyladenosine () based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.htmlThe codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git

Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background: Dimensionality reduction is the key issue of the machine learning process. It does not only improve the prediction performance but also could recommend the intrinsic features and help to explore the biological expression of the machine learning “black box”. Objective: A variety of feature selection algorithms are used to select data features to achieve dimensionality reduction. Methods: First, MRMD2.0 integrated 7 different popular feature ranking algorithms with PageRank strategy. Second, optimized dimensionality was detected with forward adding strategy. Result: We have achieved good results in our experiments. Conclusion: Several works have been tested with MRMD2.0. It showed well performance. Otherwise, it also can draw the performance curves according to the feature dimensionality. If users want to sacrifice accuracy for fewer features, they can select the dimensionality from the performance curves. Other: We developed friendly python tools together with the web server. The users could upload their csv, arff or libsvm format files. Then the webserver would help to rank features and find the optimized dimensionality.

Abstract: Objective: To investigate the effect of compound 962 capsule (abbre. as 962) on liver lipid peroxidation and indexes of thymus and spleen in aged rats.Methods: Rats were divided into young control, aged model, Piracetam, 962 middle dose (0.9 g/kg) and high dose (1.8 g/kg) groups. All test drugs were administrated for 1 month by gastrogavage. The liver lipid peroxidation was determined by thiobarbituric (TBA) method. The thymus index and spleen index were determined by weighing method.Results: 962 (middle and high dose) attenuated liver lipid peroxidation, increased the thymus index of aged rats. There was no effect on spleen index in all drug-treated groups.Conclusion: 962 could decrease liver lipid peroxidation and increase thymus index. It suggested that 962 might be beneficial in retarding aging process.