Fang Ge

Orcid: 0000-0001-5792-5379

According to our database1, Fang Ge authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.
J. Chem. Inf. Model., February, 2024

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion.
J. Chem. Inf. Model., February, 2024

Prediction of protein-ATP binding residues using multi-view feature learning via contextual-based co-attention network.
Comput. Biol. Medicine, 2024

2023
MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.
J. Chem. Inf. Model., November, 2023

VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.
Briefings Bioinform., January, 2023

Prediction of Multiple Types of RNA Modifications via Biological Language Model.
IEEE ACM Trans. Comput. Biol. Bioinform., 2023

2022
csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames.
Briefings Bioinform., November, 2022

DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies.
IEEE ACM Trans. Comput. Biol. Bioinform., 2022

PROST: AlphaFold2-aware Sequence-Based Predictor to Estimate Protein Stability Changes upon Missense Mutations.
J. Chem. Inf. Model., 2022

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.
Briefings Bioinform., 2022

2021
Accurate multistage prediction of protein crystallization propensity using deep-cascade forest with sequence-based features.
Briefings Bioinform., 2021

2020
SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network.
J. Chem. Inf. Model., 2020


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