scholarly journals Machine Learning Approach to Predicting Stem Cell Donor Availability

2018 ◽  
Vol 24 (12) ◽  
pp. 2425-2432 ◽  
Author(s):  
Adarsh Sivasankaran ◽  
Eric Williams ◽  
Mark Albrecht ◽  
Galen E. Switzer ◽  
Vladimir Cherkassky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Learning Approach ◽  
Cell Donor ◽  
Machine Learning Approach

scholarly journals Machine Learning Approach to Predicting Stem-Cell Donor Availabilitys

2018 ◽  
Author(s):  
Adarsh Sivasankaran ◽  
Eric Williams ◽  
Mark Albrecht ◽  
Galen E. Switzer ◽  
Vladimir Cherkassky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Unrelated Donor ◽  
Learning Approach ◽  
Donor Level ◽  
Machine Learning Approach ◽  
Cell Transplants ◽  
Individual Consideration ◽  
Donor Characteristics ◽  
Individual Donor

0. AbstractThe success of Unrelated Donor stem-cell transplants depends not only on finding genetically matched donors but also on donor availability. On average 50% of potential donors in the NMDP database are unavailable for a variety of reasons, after initially matching a patient, with significant variations in availability among subgroups (e.g., by race or age). Several studies have established univariate donor characteristics associated with availability. Individual consideration of each applicable characteristic is laborious. Extrapolating group averages to individual donor level tends to be highly inaccurate. In the current environment with enhanced donor data collection, we can make better estimates of individual donor availability. In this study, we propose a Machine Learning based approach to predict availability of every registered donor, to be used during donor selection and reduce the time taken to complete a transplant.

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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