An Effective Machine Learning Approach for Prognosis of Paraquat Poisoning Patients Using Blood Routine Indexes

2016 ◽  
Vol 120 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Huiling Chen ◽  
Lufeng Hu ◽  
Huaizhong Li ◽  
Guangliang Hong ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Paraquat Poisoning ◽  
Machine Learning Approach ◽  
Blood Routine

scholarly journals An Effective Machine Learning Approach for Identifying the Glyphosate Poisoning Status in Rats Using Blood Routine Test

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 15653-15662 ◽  
Author(s):  
Jiayin Zhu ◽  
Xuehua Zhao ◽  
Huaizhong Li ◽  
Huiling Chen ◽  
Gang Wu
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Routine Test ◽  
Machine Learning Approach ◽  
Blood Routine Test ◽  
Blood Routine

Metabolomics Analysis in Acute Paraquat Poisoning Patients Based on UPLC-Q-TOF-MS and Machine Learning Approach

2019 ◽  
Vol 32 (4) ◽  
pp. 629-637 ◽  
Author(s):  
Congcong Wen ◽  
Feiyan Lin ◽  
Binge Huang ◽  
Zhiguang Zhang ◽  
Xianqin Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Paraquat Poisoning ◽  
Machine Learning Approach ◽  
Metabolomics Analysis ◽  
Tof Ms

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

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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