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Predicting protein residue―residue contacts using deep networks and boosting

Bioinformatics, 2012-12, Vol.28 (23), p.3066-3072

2012

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A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

IEEE/ACM transactions on computational biology and bioinformatics, 2015-01, Vol.12 (1), p.103-112

2015

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Improving Protein Fold Recognition by Deep Learning Networks

Scientific reports, 2015-12, Vol.5 (1), p.17573-17573, Article 17573

2015

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Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

ACS applied materials & interfaces, 2019-05, Vol.11 (20), p.18494-18503

2019

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PreDisorder: ab initio sequence-based prediction of protein disordered regions

BMC bioinformatics, 2009-12, Vol.10 (1), p.436-436, Article 436

2009

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APOLLO: a quality assessment service for single and multiple protein models

Bioinformatics (Oxford, England), 2011-06, Vol.27 (12), p.1715-1716

2011

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DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning

BMC bioinformatics, 2011-02, Vol.12 (1), p.43-43, Article 43

2011

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Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Ecology and evolution, 2020-09, Vol.10 (17), p.9313-9325

2020

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Semi-Automated Inquiry of Fish Launch Angle and Speed for Hazard Analysis

Fishes, 2023-10, Vol.8 (10), p.476

2023

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An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio

Remote sensing (Basel, Switzerland), 2022-08, Vol.14 (15), p.3816

2022

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An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

International journal of molecular sciences, 2015-07, Vol.16 (7), p.15384-15404

2015

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The MULTICOM toolbox for protein structure prediction

BMC bioinformatics, 2012-04, Vol.13 (1), p.65-65, Article 65

2012

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Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

Journal of medical Internet research, 2015-06, Vol.17 (6), p.e154-e154

2015

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DNdisorder: predicting protein disorder using boosting and deep networks

BMC bioinformatics, 2013-03, Vol.14 (1), p.88-88, Article 88

2013

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Supporting Project-Based Learning through Economical and Flexible Learning Spaces

Education sciences, 2019-09, Vol.9 (3), p.212

2019

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Designing and benchmarking the MULTICOM protein structure prediction system

BMC structural biology, 2013-02, Vol.13 (1), p.2-2, Article 2

2013

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Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics

2020 IEEE Frontiers in Education Conference (FIE), 2020, p.1-5

Eickholt, Jesse

2020

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A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

BMC bioinformatics, 2013-10, Vol.14 Suppl 14 (S14), p.S12-S12, Article S12

2013

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Machine Learning Screening of Metal-Ion Battery Electrode Materials

ACS applied materials & interfaces, 2021-11, Vol.13 (45), p.53355-53362

2021

Volltextzugriff (PDF)

A comprehensive overview of computational protein disorder prediction methods

Molecular bioSystems, 2012-01, Vol.8 (1), p.114-121

2012

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Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish

ICES journal of marine science, 2020-12, Vol.77 (7-8), p.2804-2813

2020

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Practical Active Learning Stations to Transform Existing Learning Environments Into Flexible, Active Learning Classrooms

IEEE transactions on education, 2021-05, Vol.64 (2), p.95-102

2021

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MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8

Bioinformatics, 2010-04, Vol.26 (7), p.882-888

2010

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Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11

Scientific reports, 2016-01, Vol.6 (1), p.19301-19301, Article 19301

2016

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Machine Learning Screening of Metal-Ion Battery Electrode Materials

ACS applied materials & interfaces, 2021-06, Vol.13 (45)

2021

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Predicting protein residue―residue contacts using deep networks and boosting

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

Improving Protein Fold Recognition by Deep Learning Networks

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

PreDisorder: ab initio sequence-based prediction of protein disordered regions

APOLLO: a quality assessment service for single and multiple protein models

DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning

Applications of deep convolutional neural networks to predict length, circumference, and weight from mostly dewatered images of fish

Semi-Automated Inquiry of Fish Launch Angle and Speed for Hazard Analysis

An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio

An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

The MULTICOM toolbox for protein structure prediction

Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

DNdisorder: predicting protein disorder using boosting and deep networks

Supporting Project-Based Learning through Economical and Flexible Learning Spaces

Designing and benchmarking the MULTICOM protein structure prediction system

Supporting Instructor Reflection on Employed Teaching Techniques via Multimodal Instructor Analytics

A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

Machine Learning Screening of Metal-Ion Battery Electrode Materials

A comprehensive overview of computational protein disorder prediction methods

Advancements towards selective barrier passage by automatic species identification: applications of deep convolutional neural networks on images of dewatered fish

Practical Active Learning Stations to Transform Existing Learning Environments Into Flexible, Active Learning Classrooms

MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8

Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11

Machine Learning Screening of Metal-Ion Battery Electrode Materials

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