Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features

2021 ◽  
pp. 177-191
Author(s):  
Natalia V. Revollo ◽  
G. Noelia Revollo Sarmiento ◽  
Claudio Delrieux ◽  
Marcela Herrera ◽  
Rolando González-José
Keyword(s):  
Machine Learning ◽  
Human Sperm ◽  
Sperm Head ◽  
Morphological Features ◽  
Supervised Machine Learning ◽  
Machine Learning Classification

scholarly journals Machine Learning Classification of Disrotatory IRC and Conrotatory Non-IRC Trajectory Motion for Cyclopropyl Radical Ring Opening

2021 ◽  
Author(s):  
Steven Maley ◽  
Jesse Melville ◽  
Spencer Yu ◽  
Cal Hargis ◽  
Reid Hamilton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ring Opening ◽  
Supervised Machine Learning ◽  
Qualitative Assessment ◽  
Vibrational Modes ◽  
Radical Ring ◽  
Machine Learning Classification ◽  
Ring Opening Reaction ◽  
Learning Analysis

Transition-state features from trajectories were used for supervised machine learning analysis of the cyclopropyl radical ring opening reaction. Quantitative and qualitative assessment of features controlling disrotatory IRC versus conrotatory non-IRC motion and revealed that there are two key vibrational modes where their directional combination provides prediction of pathway motion. <br>

scholarly journals Semi-supervised machine-learning classification of materials synthesis procedures

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Haoyan Huo ◽  
Ziqin Rong ◽  
Olga Kononova ◽  
Wenhao Sun ◽  
Tiago Botari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Materials Synthesis ◽  
Machine Learning Classification ◽  
Classification Of Materials

scholarly journals Classification of GABAergic interneurons by leading neuroscientists

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Bojan Mihaljević ◽  
Ruth Benavides-Piccione ◽  
Concha Bielza ◽  
Pedro Larrañaga ◽  
Javier DeFelipe
Keyword(s):  
Machine Learning ◽  
Morphological Features ◽  
Supervised Machine Learning ◽  
Gabaergic Interneuron ◽  
Gabaergic Interneurons ◽  
High Agreement ◽  
Anatomical Characteristics

Abstract There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classification choices by the neuroscientists according to interneuron type as well as to five prominent morphological features. These data can be used as crisp or soft training labels for learning supervised machine learning interneuron classifiers, while further analyses can try to pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.

scholarly journals Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 778
Author(s):  
Nitsa J. Herzog ◽  
George D. Magoulas
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Progressive Dementia ◽  
Machine Learning Classification

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.

scholarly journals Machine Learning Classification of Disrotatory IRC and Conrotatory Non-IRC Trajectory Motion for Cyclopropyl Radical Ring Opening

2021 ◽  
Author(s):  
Steven Maley ◽  
Jesse Melville ◽  
Spencer Yu ◽  
Cal Hargis ◽  
Reid Hamilton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ring Opening ◽  
Supervised Machine Learning ◽  
Qualitative Assessment ◽  
Vibrational Modes ◽  
Radical Ring ◽  
Machine Learning Classification ◽  
Ring Opening Reaction ◽  
Learning Analysis

Transition-state features from trajectories were used for supervised machine learning analysis of the cyclopropyl radical ring opening reaction. Quantitative and qualitative assessment of features controlling disrotatory IRC versus conrotatory non-IRC motion and revealed that there are two key vibrational modes where their directional combination provides prediction of pathway motion. <br>

Bayesian supervised machine learning classification of neural networks with pathological perturbations

2021 ◽  
Vol 7 (6) ◽  
pp. 065021
Author(s):  
Riccardo Levi ◽  
Vibeke Devold Valderhaug ◽  
Salvatore Castelbuono ◽  
Axel Sandvig ◽  
Ioanna Sandvig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Machine Learning ◽  
Machine Learning Classification

Machine Learning Classification of Low-grade and High-grade Chondrosarcomas Based on MRI-based Texture Analysis

2019 ◽  
Author(s):  
S. Gitto ◽  
D. Albano ◽  
V. Chianca ◽  
R. Cuocolo ◽  
L. Ugga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Analysis ◽  
Low Grade ◽  
High Grade ◽  
Machine Learning Classification

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

2021 ◽  
Vol 79 ◽  
pp. 52-58
Author(s):  
Arnaldo Stanzione ◽  
Renato Cuocolo ◽  
Francesco Verde ◽  
Roberta Galatola ◽  
Valeria Romeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classification
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