mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization.

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules.

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch.

Cooperative Membrane Damage as a Mechanism for Pentamidine-Antibiotic Mutual Sensitization.

Most Gram-positive-selective antibiotics have low activity against Gram-negative bacteria due to the presence of an outer membrane barrier. There is, therefore, interest in developing combination therapies that can penetrate the outer membrane (OM) with known antibiotics coupled with membrane-active sensitizing adjuvants. However, two unanswered questions hinder the development of such combination therapies: the sensitization spectrum of the sensitizer and the mechanism of antibiotic-sensitizer mutual potentiation.

MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation.

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy.

iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor.

Motivation: Enhancers are non-coding DNA fragments with high position variability and free scattering. They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed.