https://socictopen.socict.org/files/original/5c82fab934b2861fe7c0df5310397c16.pdf f2000ba57d4d11a975b5d5232b6663d0 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information Creator An entity primarily responsible for making the resource Zhao-Hui Zhan, Lina Jia, Yong Zhou, Liping Li, Hai-Cheng Yi Description An account of the resource The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research. Date A point or period of time associated with an event in the lifecycle of the resource 2019 Subject The topic of the resource ncRNA-protein interaction, bi-gram, Position specific scoring matrix, Kmers, deep learning Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/ijms20040978 Source A related resource from which the described resource is derived International Journal of Molecular Sciences Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Chemistry Language A language of the resource EN