scholarly journals Correction: The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244509
Author(s):  
Michael Barth ◽  
Arne Güllich ◽  
Christian Raschner ◽  
Eike Emrich
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Specific Practice ◽  
The Impact

scholarly journals The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0239378 ◽  
Author(s):  
Michael Barth ◽  
Arne Güllich ◽  
Christian Raschner ◽  
Eike Emrich
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Specific Practice ◽  
The Impact

The impact of economic plans on the Chinese education system: a machine learning approach

CADMO ◽  
2018 ◽  
pp. 37-49
Author(s):  
Wenjun Lin ◽  
Xuefu Xu ◽  
Francesco Dell’Anna
Keyword(s):  
Machine Learning ◽  
Education System ◽  
Learning Approach ◽  
Chinese Education ◽  
System A ◽  
Machine Learning Approach ◽  
The Impact

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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