scholarly journals Personalized quantification of facial normality: a machine learning approach

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Osman Boyaci ◽  
Erchin Serpedin ◽  
Mitchell A. Stotland
Keyword(s):  
Paradigm Shift ◽  
Surgical Planning ◽  
Outcome Measurement ◽  
Great Promise ◽  
Learning Approach ◽  
Human Face ◽  
Facial Image ◽  
Perceptual Distance ◽  
Machine Learning Approach ◽  
Objective Tool

AbstractWhat is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically along their own continuum of normality, and to measure any surgical changes against such a personalized benchmark. This has not previously been possible. We have solved this problem by designing a computerized model that produces realistic, normalized versions of any given facial image, and objectively measures the perceptual distance between the raw and normalized facial image pair. The model is able to faithfully predict human scoring of facial normality. We believe this work represents a paradigm shift in the assessment of the human face, holding great promise for development as an objective tool for surgical planning, patient education, and as a means for clinical outcome measurement.

scholarly journals A Machine Learning Approach to Study Demographic Alterations in Honeybee Colonies Using SDS–PAGE Fingerprinting

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1823
Author(s):  
Riccardo Cabbri ◽  
Enea Ferlizza ◽  
Elisa Bellei ◽  
Giulia Andreani ◽  
Roberta Galuppi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Paradigm Shift ◽  
Learning Algorithm ◽  
Multiple Stressors ◽  
Emergent Properties ◽  
Learning Approach ◽  
Nearest Neighbours ◽  
Sds Page ◽  
Machine Learning Approach ◽  
Negative Factors

Honeybees, as social insects, live in highly organised colonies where tasks reflect the age of individuals. As is widely known, in this context, emergent properties arise from interactions between them. The accelerated maturation of nurses into foragers, stimulated by many negative factors, may disrupt this complex equilibrium. This complexity needs a paradigm shift: from the study of a single stressor to the study of the effects exerted by multiple stressors on colony homeostasis. The aim of this research is, therefore, to study colony population disturbances by discriminating overaged nurses from proper aged nurses and precocious foragers from proper aged foragers using SDS-PAGE patterns of haemolymph proteins and a machine-learning algorithm. The KNN (K Nearest Neighbours) model fitted on the forager dataset showed remarkably good performances (accuracy 0.93, sensitivity 0.88, specificity 1.00) in discriminating precocious foragers from proper aged ones. The main strength of this innovative approach lies in the possibility of it being deployed as a preventive tool. Depopulation is an elusive syndrome in bee pathology and early detection with the method described could shed more light on the phenomenon. In addition, it enables countermeasures to revert this vicious circle.

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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