scholarly journals A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers

2021 ◽  
Vol 8 (1) ◽  
pp. 64-76
Author(s):  
Cosimo Ieracitano ◽  
Annunziata Paviglianiti ◽  
Maurizio Campolo ◽  
Amir Hussain ◽  
Eros Pasero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Electrospun Nanofibers ◽  
Automatic Classification ◽  
Supervised Machine Learning

A Method for Sensor Reduction in a Supervised Machine Learning Classification System

2019 ◽  
Vol 24 (1) ◽  
pp. 197-206
Author(s):  
Niko Murrell ◽  
Ryan Bradley ◽  
Nikhil Bajaj ◽  
Julie Gordon Whitney ◽  
George T.-C. Chiu
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Supervised Machine Learning ◽  
Machine Learning Classification ◽  
Sensor Reduction

scholarly journals Automatic classification of white regions in liver biopsies by supervised machine learning

2014 ◽  
Vol 45 (4) ◽  
pp. 785-792 ◽  
Author(s):  
Scott Vanderbeck ◽  
Joseph Bockhorst ◽  
Richard Komorowski ◽  
David E. Kleiner ◽  
Samer Gawrieh
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Supervised Machine Learning ◽  
Liver Biopsies

scholarly journals Identification and prediction of ALS subgroups using machine learning

2021 ◽  
Author(s):  
Faraz Faghri ◽  
Fabian Brunn ◽  
Anant Dadu ◽  
Elisabetta Zucchi ◽  
Ilaria Martinelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Population Based ◽  
Classification Systems ◽  
Supervised Machine Learning ◽  
Population Substructure ◽  
Clinical Parameters ◽  
Independent Population ◽  
Als Patients ◽  
Clinical Subtype

Background The disease entity known as amyotrophic lateral sclerosis (ALS) is now known to represent a collection of overlapping syndromes. A better understanding of this heterogeneity and the ability to distinguish ALS subtypes would improve the clinical care of patients and enhance our understanding of the disease. Subtype profiles could be incorporated into the clinical trial design to improve our ability to detect a therapeutic effect. A variety of classification systems have been proposed over the years based on empirical observations, but it is unclear to what extent they genuinely reflect ALS population substructure. Methods We applied machine learning algorithms to a prospective, population-based cohort consisting of 2,858 Italian patients diagnosed with ALS for whom detailed clinical phenotype data were available. We replicated our findings in an independent population-based cohort of 1,097 Italian ALS patients. Findings We found that semi-supervised machine learning based on UMAP applied to the output of a multi-layered perceptron neural network produced the optimum clustering of the ALS patients in the discovery cohort. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (bulbar ALS, respiratory ALS, flail arm ALS, classical ALS, pyramidal ALS, and flail leg ALS). The same clusters were identified in the replication cohort. A supervised learning approach based on ensemble learning identified twelve clinical parameters that predicted ALS clinical subtype with high accuracy (area under the curve = 0.94). Interpretation Our data-driven study provides insight into the ALS population's substructure and demonstrates that the Chiò classification system robustly identifies ALS subtypes. We provide an interactive website (https://share.streamlit.io/anant-dadu/machinelearningforals/main) so that clinical researchers can predict the clinical subtype of an ALS patient based on a small number of clinical parameters. Funding National Institute on Aging and the Italian Ministry of Health.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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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