AI Medical Compendium Journal:
RNA (New York, N.Y.)

Showing 1 to 5 of 5 articles

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...

De novo prediction of RNA-protein interactions with graph neural networks.

RNA (New York, N.Y.)
RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein ...

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA (New York, N.Y.)
RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to qua...

Expanding the horizons of microRNA bioinformatics.

RNA (New York, N.Y.)
MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult fo...

miRBoost: boosting support vector machines for microRNA precursor classification.

RNA (New York, N.Y.)
Identification of microRNAs (miRNAs) is an important step toward understanding post-transcriptional gene regulation and miRNA-related pathology. Difficulties in identifying miRNAs through experimental techniques combined with the huge amount of data ...