Jianfeng Sun
  • Bio
  • CV
  • Publications
  • News
  • Posts
  • Talks
  • Experiences
  • Projects
  • Software
  • Teaching
  • Posts
    • A unified ecosystem established to integrate UMI-centric analyses for improved molecular quantification accuracy
    • scRNA-seq analysis learning note-1
  • Softwares
  • Softwares
    • Tresor
    • mclUMI
    • UMIche
    • PyPropel
    • PCSER
    • ResimPy
    • TMKit
    • DeepdlncUD
    • Drutai
    • DeepsmirUD
    • DeepTMInter
    • DeepHelicon
  • News
    • 🎉 Publication in Nature Communications (11.03.2025)
    • 🎉 Publication in Communications Biology (16.01.2025)
    • 🎉 Full member of Sigma Xi
    • 🎉 Publication in Nature Methods (Volume 21 Issue 3, March 2024)
  • Publications
    • SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants
    • PyPropel: a Python-based tool for efficiently processing and characterising protein data
    • Enhancing single-cell transcriptomics using interposed anchor oligonucleotide sequences
    • mclUMI: Markov clustering of unique molecular identifiers enables dynamic removal of PCR duplicates
    • Tresor: An integrated platform for simulating transcriptomic reads with realistic PCR error representation across various RNA sequencing technologies
    • UMIche: A platform for robust UMI-centric simulation and analysis in bulk and single-cell sequencing
    • Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes
    • An Oral Nanomedicine Elicits In Situ Vaccination Effect against Colorectal Cancer
    • Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules
    • PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction
    • Transient Mild Photothermia Improves Therapeutic Performance of Oral Nanomedicines with Enhanced Accumulation in the Colitis Mucosa
    • TMKit: a Python interface for computational analysis of transmembrane proteins
    • Bioinspired Polyacrylic Acid-Based Dressing: Wet Adhesive, Self-Healing, and Multi-Biofunctional Coacervate Hydrogel Accelerates Wound Healing
    • DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning
    • DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning
    • Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications
    • Plasma lipidome reveals susceptibility and resistance of Pekin ducks to DHAV-3
    • Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications
    • Tea leaf-derived exosome-like nanotherapeutics retard breast tumor growth by pro-apoptosis and microbiota modulation
    • Unveiling the hidden role of aquatic viruses in hydrocarbon pollution bioremediation
    • Edible plant-derived nanotherapeutics and nanocarriers: recent progress and future directions
    • Oral Nanomotor-Enabled Mucus Traverse and Tumor Penetration for Targeted Chemo-Sono-Immunotherapy against Colon Cancer
    • Oral nanotherapeutics based on Antheraea pernyi silk fibroin for synergistic treatment of ulcerative colitis
    • PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules
    • Complex Age- and Cancer-Related Changes in Human Blood Transcriptome—Implications for Pan-Cancer Diagnostics
    • Genetic hybridization of highly active exogenous functional proteins into silk-based materials using “light-clothing” strategy
    • Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning
    • Missense Variant of Endoplasmic Reticulum Region of WFS1 Gene Causes Autosomal Dominant Hearing Loss without Syndromic Phenotype
    • DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks
    • Improving Residue-Residue Contacts Prediction from Protein Sequences Using RNN-Based LSTM Network
    • E-Index for Differentiating Complex Dynamic Traits
  • Projects
    • Direct RNA-seq
    • PROTAC off-target effect prediction
    • Variant effect prediction
  • Teaching
    • Demultiplexing for molecular quantification
    • Machine learning in structural biology
  • Projects
  • Experience
  • Recent & Upcoming Talks
    • Botnar Seminar Series 2023
    • ICMLC 2019
    • PIMRC 2015

Variant effect prediction

Jul 1, 2024 · 1 min read
Go to Project Site

Disease-causing variant effects are to be quantified on transmembrane protein structures. ex. molecule: 1TQN.cif.

Last updated on Jul 1, 2024
Variant AI Deep Learning
Jianfeng Sun
Authors
Jianfeng Sun
Postdoctoral researcher
← PROTAC off-target effect prediction Jul 1, 2024

© 2025 Jianfeng Sun. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.