Junru Jin

DNA sequence sets the destination of spontaneous epimutations in plants

The paper shows DNA sequence determines the destination of spontaneous epimutations in plants, using a deep-learning model trained on one methylome to predict methylation set points across species.

Reaction-aware hypergraph learning of chemical representations with LARK

This paper introduces LARK, a framework that learns chemical representations from reactions, showing improved performance in reaction prediction and molecular property prediction.

Melody: Decoding the Sequence Determinants of Locus Specific DNA Methylation Across Human Tissues

Melody predicts DNA methylation from genomic sequences across human tissues and outperforms existing methods, also generalizing to methylation quantitative trait locus effect prediction.

Spatial-neighbour encoding enables fast RNA 3D structure search

The paper introduces RiboSeek, a framework for RNA 3D structure search using geometric alphabets, achieving fast and accurate comparisons.

Accurate detection and quantification of single-base m6A RNA modification using nanopore signals with multi-view deep learning

MultiNano uses multi-view deep learning to accurately detect and quantify m6A RNA modifications from Nanopore DRS signals, achieving state-of-the-art performance in multiple tasks.

HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification

HGCPep improves ncPEP identification by using a hypergraph model to capture peptide-RNA relationships, outperforming existing methods.

StructuralDPPIV: A novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides

StructuralDPPIV is a deep learning model that predicts DPP-IV inhibitory peptides using molecular graph and sequence features, outperforming existing methods on test datasets.

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

DeepBIO is a deep-learning platform that automates biological sequence analysis, offering 42 algorithms and visualization tools for functional prediction and annotation.

Explainable deep graph learning accurately modeling the peptide secondary structure prediction

This paper introduces PHAT, a deep graph learning framework for peptide secondary structure prediction, achieving better accuracy and interpretability for short peptides.