PyPropel: a Python-based tool for efficiently processing and characterising protein data
Sun J. et al, (2025), Bmc bioinformatics, 26
PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.
Xiong D. et al, (2024), Interdiscip sci, 16, 802 - 813
Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules.
Sun J. et al, (2024), Nat methods, 21, 401 - 405
Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.
Sun J. et al, (2023), Eur j med chem, 257
DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning.
Sun J. et al, (2023), Comput biol med, 163
Near-Infrared-Enpowered Nanomotor-Mediated Targeted Chemotherapy and Mitochondrial Phototherapy to Boost Systematic Antitumor Immunity.
Zhang X. et al, (2022), Adv healthc mater, 11
Edible plant-derived nanotherapeutics and nanocarriers: recent progress and future directions.
Chen N. et al, (2022), Expert opin drug deliv, 19, 409 - 419
Oral nanotherapeutics based on Antheraea pernyi silk fibroin for synergistic treatment of ulcerative colitis.
Ma Y. et al, (2022), Biomaterials, 282
PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules.
Yu F. et al, (2022), Front mol biosci, 9
Genetic hybridization of highly active exogenous functional proteins into silk-based materials using “light-clothing” strategy
Long D. et al, (2021), Matter, 4, 2039 - 2058
Complex Age- and Cancer-Related Changes in Human Blood Transcriptome-Implications for Pan-Cancer Diagnostics.
Qi F. et al, (2021), Front genet, 12
Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning.
Sun J. and Frishman D., (2021), Comput struct biotechnol j, 19, 1512 - 1530
Missense Variant of Endoplasmic Reticulum Region of WFS1 Gene Causes Autosomal Dominant Hearing Loss without Syndromic Phenotype.
Li J. et al, (2021), Biomed res int, 2021
DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks.
Sun J. and Frishman D., (2020), J struct biol, 212
Improving Residue-Residue Contacts Prediction from Protein Sequences Using RNN-Based LSTM Network
Chen W. et al, (2019), 2019 international conference on machine learning and cybernetics (icmlc)