David B. Ascher

Orcid: 0000-0003-2948-2413

Affiliations:
  • University of Melbourne, Bio21 Institute, Department of Biochemistry and Molecular Biology, Australia
  • Cambridge University, Department of Biochemistry, UK


According to our database1, David B. Ascher authored at least 45 papers between 2014 and 2023.

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

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2023
Characterization on the oncogenic effect of the missense mutations of p53 via machine learning.
Briefings Bioinform., November, 2023

DDMut: predicting effects of mutations on protein stability using deep learning.
Nucleic Acids Res., July, 2023

epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.
Briefings Bioinform., May, 2023

embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules.
J. Chem. Inf. Model., January, 2023

DockNet: high-throughput protein-protein interface contact prediction.
Bioinform., January, 2023

Explainable Machine Learning for ICU Readmission Prediction.
CoRR, 2023

AI driven B-cell Immunotherapy Design.
CoRR, 2023

2022
Known allosteric proteins have central roles in genetic disease.
PLoS Comput. Biol., 2022

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.
Nucleic Acids Res., 2022

<i>cardioToxCSM</i>: A Web Server for Predicting Cardiotoxicity of Small Molecules.
J. Chem. Inf. Model., 2022

Bioinformatics Approaches to Predict Mutation Effects in the Binding Site of the Proangiogenic Molecule CD93.
Frontiers Bioinform., 2022

CSM-AB: graph-based antibody-antigen binding affinity prediction and docking scoring function.
Bioinform., 2022

epitope3D: a machine learning method for conformational B-cell epitope prediction.
Briefings Bioinform., 2022

toxCSM: comprehensive prediction of small molecule toxicity profiles.
Briefings Bioinform., 2022

Structural landscapes of PPI interfaces.
Briefings Bioinform., 2022

Evaluating hierarchical machine learning approaches to classify biological databases.
Briefings Bioinform., 2022

cropCSM: designing safe and potent herbicides with graph-based signatures.
Briefings Bioinform., 2022

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures.
Briefings Bioinform., 2022

GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms.
Briefings Bioinform., 2022

CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function.
Briefings Bioinform., 2022

2021
ThermoMutDB: a thermodynamic database for missense mutations.
Nucleic Acids Res., 2021

MTR3D: identifying regions within protein tertiary structures under purifying selection.
Nucleic Acids Res., 2021

mmCSM-PPI: predicting the effects of multiple point mutations on protein-protein interactions.
Nucleic Acids Res., 2021

pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors.
J. Chem. Inf. Model., 2021

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.
J. Chem. Inf. Model., 2021

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation.
Comput. Biol. Medicine, 2021

Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations.
Briefings Bioinform., 2021

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis.
Proceedings of the Artificial Neural Networks - Third Edition., 2021

2020
mCSM-membrane: predicting the effects of mutations on transmembrane proteins.
Nucleic Acids Res., 2020

mmCSM-AB: guiding rational antibody engineering through multiple point mutations.
Nucleic Acids Res., 2020

ProCarbDB: a database of carbohydrate-binding proteins.
Nucleic Acids Res., 2020

mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria.
J. Chem. Inf. Model., 2020

EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening.
Bioinform., 2020

mCSM-AB2: guiding rational antibody design using graph-based signatures.
Bioinform., 2020

2019
MTR-Viewer: identifying regions within genes under purifying selection.
Nucleic Acids Res., 2019

mCSM-PPI2: predicting the effects of mutations on protein-protein interactions.
Nucleic Acids Res., 2019

2018
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability.
Nucleic Acids Res., 2018

Kinact: a computational approach for predicting activating missense mutations in protein kinases.
Nucleic Acids Res., 2018

2017
mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions.
Nucleic Acids Res., 2017

SDM: a server for predicting effects of mutations on protein stability.
Nucleic Acids Res., 2017

2016
CSM-lig: a web server for assessing and comparing protein-small molecule affinities.
Nucleic Acids Res., 2016

mCSM-AB: a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures.
Nucleic Acids Res., 2016

2015
Platinum: a database of experimentally measured effects of mutations on structurally defined protein-ligand complexes.
Nucleic Acids Res., 2015

2014
DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
Nucleic Acids Res., 2014

mCSM: predicting the effects of mutations in proteins using graph-based signatures.
Bioinform., 2014


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