about
A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks.The MULTICOM protein tertiary structure prediction system.PCP-ML: protein characterization package for machine learning.NNcon: improved protein contact map prediction using 2D-recursive neural networksAn Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.Prediction of global and local quality of CASP8 models by MULTICOM series.PreDisorder: ab initio sequence-based prediction of protein disordered regions.MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning.APOLLO: a quality assessment service for single and multiple protein models.A comprehensive overview of computational protein disorder prediction methodsThe MULTICOM toolbox for protein structure predictionRecursive protein modeling: a divide and conquer strategy for Protein Structure Prediction and its case study in CASP9Designing and benchmarking the MULTICOM protein structure prediction systemDNdisorder: predicting protein disorder using boosting and deep networks.A conformation ensemble approach to protein residue-residue contact.A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.Predicting protein residue-residue contacts using deep networks and boostingBenchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11Improving Protein Fold Recognition by Deep Learning Networks.An underwater observation dataset for fish classification and fishery assessmentA distributed pipeline for DIDSON data processing
P50
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P50
name
Jesse Eickholt
@en
Jesse Eickholt
@nl
type
label
Jesse Eickholt
@en
Jesse Eickholt
@nl
prefLabel
Jesse Eickholt
@en
Jesse Eickholt
@nl