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Articles by Junru Jin
Decoding the sequence determinants of locus-specific DNA methylation across human tissues
Abstract DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals.
ABT-DDI: A Graph Transformer Model with Atomic-Bond Structure Awareness for Drug–Drug Interaction Prediction
Abstract Click to copy section linkSection link copied! The widespread use of polypharmacy has significantly increased the risk of drug–drug interactions (DDIs), underscoring the critical need for developing accurate drug–drug interaction events (DDIEs) prediction methods. However, current DDI studies inadequately account for the intrinsic relationships between atoms and bonds in drug molecules, while also overlooking the three-dimensional conformational information on these molecules.
HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification
Abstract The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm of cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. The identification and characterization of cancer-associated ncPEPs assume a crucial role in propelling cancer research forward and augmenting our comprehension of immune-related processes.
Moss-m7G: A Motif-Based Interpretable Deep Learning Method for RNA N7-Methlguanosine Site Prediction
StructuralDPPIV: A novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides
Abstract Background: Diabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation across the world. To alleviate the impact of diabetes, researchers have developed the next generation of anti-diabetic drugs, known as dipeptidyl peptidase IV inhibitory peptides (DPP-IV-IPs). However, the discovery of these promising drugs has been restricted due to the lack of effective peptide-mining tools.
StructuralDPPIV: A novel deep learning model based on atom-structure for predicting dipeptidyl peptidase-IV inhibitory peptides
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DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis
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DeepBIO: An automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation, and visualization analysis
Abstract Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop new deep-learning architectures to answer any biological question.
DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis
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Explainable deep graph learning accurately modeling the peptide secondary structure prediction
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PHAT: interpretable prediction of peptide secondary structures using hypergraph multi-head attention network and transfer learning
New Results doi: https://doi.org/10.1101/2022.06.09.495580 Abstract Peptides have recently emerged as therapeutic molecules against various diseases, and the secondary structure of peptides is a crucial determinant of their bioactivity. However, accurately predicting peptide secondary structures remains a challenging task due to the lack of peptide sequence data and low prediction efficiency caused by limitations in feature engineering.
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