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Enzyme structure correlates with variant effect predictability

Computational and structural biotechnology journal, 2024-12, Vol.23, p.3489-3497

2024

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The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology

Frontiers in Systems Biology (Online), 2024-08, Vol.4

2024

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Full-length single-molecule protein fingerprinting

Nature nanotechnology, 2024-05, Vol.19 (5), p.652-659

2024

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Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning

Nucleic acids research, 2023-03, Vol.51 (5), p.2363-2376

2023

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Genomic prediction in plants: opportunities for ensemble machine learning based approaches [version 2; peer review: 1 approved, 2 approved with reservations]

F1000 research, 2023, Vol.11, p.802-802

2023

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Beyond sequence: Structure-based machine learning

Computational and structural biotechnology journal, 2023-01, Vol.21, p.630-643

2023

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Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS

G3 : genes - genomes - genetics, 2022-11, Vol.12 (11)

2022

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Editorial: Artificial Intelligence and Machine Learning Applications in Plant Genomics and Genetics

Frontiers in artificial intelligence, 2022-06, Vol.5, p.959470-959470

2022

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Genomic prediction in plants: opportunities for ensemble machine learning based approaches [version 1; peer review: 1 approved, 1 approved with reservations]

F1000 research, 2022, Vol.11, p.802

2022

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Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

PLoS computational biology, 2021-03, Vol.17 (3), p.e1008197-e1008197

2021

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Geometricus represents protein structures as shape-mers derived from moment invariants

Bioinformatics, 2020-12, Vol.36 (Supplement_2), p.i718-i725

2020

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CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Genome medicine, 2020-08, Vol.12 (1), p.1-75, Article 75

2020

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Designing Eukaryotic Gene Expression Regulation Using Machine Learning

Trends in biotechnology (Regular ed.), 2020-02, Vol.38 (2), p.191-201

2020

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Caretta – A multiple protein structure alignment and feature extraction suite

Computational and structural biotechnology journal, 2020-01, Vol.18, p.981-992

2020

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Hecaton: reliably detecting copy number variation in plant genomes using short read sequencing data

BMC genomics, 2019-11, Vol.20 (1), p.818-818, Article 818

2019

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Artificial intelligence in the lab: ask not what your computer can do for you

Microbial biotechnology, 2019-01, Vol.12 (1), p.38-40

Ridder, Dick

2019

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DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom

The Plant journal : for cell and molecular biology, 2018-08, Vol.95 (4), p.686-699

2018

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Sequence‐based analysis of protein degradation rates

Proteins, structure, function, and bioinformatics, 2017-09, Vol.85 (9), p.1593-1601

2017

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Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

PLoS computational biology, 2017-09, Vol.13 (9), p.e1005734

2017

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A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data

Scientifica (Cairo), 2017-01, Vol.2017 (2017), p.1-8

2017

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Selected proceedings of Machine Learning in Systems Biology: MLSB 2016

BMC bioinformatics, 2016-12, Vol.17 (Suppl 16), p.437-437, Article 437

2016

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Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset

Scientifica (Cairo), 2016-01, Vol.2016 (2016), p.1-6

2016

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Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger

PloS one, 2012-10, Vol.7 (10), p.e45869-e45869

2012

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Enzyme structure correlates with variant effect predictability

The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology

Full-length single-molecule protein fingerprinting

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning

Genomic prediction in plants: opportunities for ensemble machine learning based approaches [version 2; peer review: 1 approved, 2 approved with reservations]

Beyond sequence: Structure-based machine learning

Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS

Editorial: Artificial Intelligence and Machine Learning Applications in Plant Genomics and Genetics

Genomic prediction in plants: opportunities for ensemble machine learning based approaches [version 1; peer review: 1 approved, 1 approved with reservations]

Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

Geometricus represents protein structures as shape-mers derived from moment invariants

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Designing Eukaryotic Gene Expression Regulation Using Machine Learning

Caretta – A multiple protein structure alignment and feature extraction suite

Hecaton: reliably detecting copy number variation in plant genomes using short read sequencing data

Artificial intelligence in the lab: ask not what your computer can do for you

DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom

Sequence‐based analysis of protein degradation rates

Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data

Selected proceedings of Machine Learning in Systems Biology: MLSB 2016

Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset

Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger

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