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RNA

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Science fact vs science fiction: A ChatGPT immunological review experiment gone awry.

Immunology letters
Artificial intelligence (AI) has made great progress in recent years. The latest chatbot to make a splash is ChatGPT. To see if this type of AI could also be helpful in creating an immunological review article, I put a planned review on different cla...

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many...

CRMSNet: A deep learning model that uses convolution and residual multi-head self-attention block to predict RBPs for RNA sequence.

Proteins
RNA-binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA-protein binding sites. Deep learning algorithms based on tradit...

Evaluating native-like structures of RNA-protein complexes through the deep learning method.

Nature communications
RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computation...

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction.

Scientific reports
The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures o...

Machine Learning Informs RNA-Binding Chemical Space.

Angewandte Chemie (International ed. in English)
Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders...

iDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficie...

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.

RNA (New York, N.Y.)
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowled...

Predicting RNA secondary structure by a neural network: what features may be learned?

PeerJ
Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data m...

Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.

International journal of molecular sciences
N6-methyladenosine (mA) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA ...