Investigating deep feedforward neural networks for classification of transposon-derived piRNAs

Characterization of rice starch changes in saline and alkaline area under different fertilization conditions based on Raman spectral recognition technology

Starch content in rice is one of the important parameters in characterizing the nutritional quality of rice, and the starch content of rice produced in saline soils under different fertilization conditions varies. In this study, Raman spectroscopy combined with three machine learning models, support vector machine (SVM), feedforward neural network, and k-nearest neighbor classification, was used to classify and evaluate the effect of different fertilizer treatments on rice. The collected rice spectral data were normalized before machine learning, then preprocessed with multiple scattering correction (MSC), standard normal variable, and Savitzky-Golay filtering algorithms to improve the quality and reliability of the data. The evaluation indexes such as the confusion matrix and the receiver operating characteristic curve comprehensively analyzed the model's performance. The research shows that the MSC preprocessing method significantly improves the classification accuracy and prediction ability in all three models, and the classification accuracy was close to 100%, while the overall performance of the SVM models after various preprocessing is the best among the three machine learning methods. The predictive coefficient of determination, predictive root mean square error, and predictive average relative error of the starch content detection model built by the SVM model after MSC preprocessing were 0.93, 0.04%, and 0.20%, respectively, which indicated that its prediction had high accuracy and low error. The results of this study used Raman spectroscopy to carry out the identification of different fertilization techniques and rice starch quality correlation characteristics, providing theoretical and experimental support for the rapid identification of rice quality.

Databases and computational methods for the identification of piRNA-related molecules: A survey

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.

PIWI-interacting RNAs in human diseases: databases and computational models

PIWI-interacting RNAs (piRNAs) are short 21-35 nucleotide molecules that comprise the largest class of non-coding RNAs and found in a large diversity of species including yeast, worms, flies, plants and mammals including humans. The most well-understood function of piRNAs is to monitor and protect the genome from transposons particularly in germline cells. Recent data suggest that piRNAs may have additional functions in somatic cells although they are expressed there in far lower abundance. Compared with microRNAs (miRNAs), piRNAs have more limited bioinformatics resources available. This review collates 39 piRNA specific and non-specific databases and bioinformatics resources, describes and compares their utility and attributes and provides an overview of their place in the field. In addition, we review 33 computational models based upon function: piRNA prediction, transposon element and mRNA-related piRNA prediction, cluster prediction, signature detection, target prediction and disease association. Based on the collection of databases and computational models, we identify trends and potential gaps in tool development. We further analyze the breadth and depth of piRNA data available in public sources, their contribution to specific human diseases, particularly in cancer and neurodegenerative conditions, and highlight a few specific piRNAs that appear to be associated with these diseases. This briefing presents the most recent and comprehensive mapping of piRNA bioinformatics resources including databases, models and tools for disease associations to date. Such a mapping should facilitate and stimulate further research on piRNAs.