ncDLRES: a novel method for non-coding RNAs family prediction based on dynamic LSTM and ResNet

The role of long noncoding RNAs in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a poor prognosis leading to death. The diagnosis and treatment of ALS are inherently challenging due to its complex pathomechanism. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides involved in different cellular processes, incisively gene expression. In recent years, more studies have been conducted on lncRNA classes and interference in different disease pathologies, showing their promising contribution to diagnosing and treating neurodegenerative diseases. In this review, we discussed the role of lncRNAs like NEAT1 and C9orf72-as in ALS pathogenesis mechanisms caused by mutations in different genes, including TAR DNA-binding protein-43 (TDP-43), fused in sarcoma (FUS), superoxide dismutase type 1 (SOD1). NEAT1 is a well-established lncRNA in ALS pathogenesis; hence, we elaborate on its involvement in forming paraspeckles, stress response, inflammatory response, and apoptosis. Furthermore, antisense lncRNAs (as-lncRNAs), a key group of transcripts from the opposite strand of genes, including ZEB1-AS1 and ATXN2-AS, are discussed as newly identified components in the pathology of ALS. Ultimately, we review the current standing of using lncRNAs as biomarkers and therapeutic agents and the future vision of further studies on lncRNA applications.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

DeeProPre: A promoter predictor based on deep learning

The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master.