 Unsupervised Machine Learning
"Unsupervised Machine Learning" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
| Descriptor ID |
D000069558
|
| MeSH Number(s) |
G17.035.250.500.750 L01.224.050.375.530.750
|
| Concept/Terms |
|
Below are MeSH descriptors whose meaning is more general than "Unsupervised Machine Learning".
Below are MeSH descriptors whose meaning is more specific than "Unsupervised Machine Learning".
This graph shows the total number of publications written about "Unsupervised Machine Learning" by people in this website by year, and whether "Unsupervised Machine Learning" was a major or minor topic of these publications.
To see the data from this visualization as text, click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 2015 | 0 | 1 | 1 | | 2018 | 1 | 1 | 2 | | 2019 | 0 | 1 | 1 | | 2022 | 0 | 1 | 1 | | 2023 | 0 | 1 | 1 |
To return to the timeline, click here.
Below are the most recent publications written about "Unsupervised Machine Learning" by people in Profiles.
-
Masaracchia L, Fredes F, Woolrich MW, Vidaurre D. Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data. J Neurophysiol. 2023 08 01; 130(2):364-379.
-
Angelini ED, Yang J, Balte PP, Hoffman EA, Manichaikul AW, Sun Y, Shen W, Austin JHM, Allen NB, Bleecker ER, Bowler R, Cho MH, Cooper CS, Couper D, Dransfield MT, Garcia CK, Han MK, Hansel NN, Hughes E, Jacobs DR, Kasela S, Kaufman JD, Kim JS, Lappalainen T, Lima J, Malinsky D, Martinez FJ, Oelsner EC, Ortega VE, Paine R, Post W, Pottinger TD, Prince MR, Rich SS, Silverman EK, Smith BM, Swift AJ, Watson KE, Woodruff PG, Laine AF, Barr RG. Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans. Thorax. 2023 11; 78(11):1067-1079.
-
Yuan NF, Hasenstab K, Retson T, Conrad DJ, Lynch DA, Hsiao A. Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease. Ann Am Thorac Soc. 2022 Dec; 19(12):1993-2002.
-
Taroni JN, Grayson PC, Hu Q, Eddy S, Kretzler M, Merkel PA, Greene CS. MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease. Cell Syst. 2019 05 22; 8(5):380-394.e4.
-
Lusk R, Saba LM, Vanderlinden LA, Zidek V, Silhavy J, Pravenec M, Hoffman PL, Tabakoff B. Unsupervised, Statistically Based Systems Biology Approach for Unraveling the Genetics of Complex Traits: A Demonstration with Ethanol Metabolism. Alcohol Clin Exp Res. 2018 07; 42(7):1177-1191.
-
Way GP, Greene CS. Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Pac Symp Biocomput. 2018; 23:80-91.
-
Ross J, Neylan T, Weiner M, Chao L, Samuelson K, Sim I. Towards Constructing a New Taxonomy for Psychiatry Using Self-reported Symptoms. Stud Health Technol Inform. 2015; 216:736-40.
-
Tan J, Ung M, Cheng C, Greene CS. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac Symp Biocomput. 2015; 132-43.
|
People  People who have written about this concept. _
Similar Concepts
People who have written about this concept.
_
Top Journals
Top journals in which articles about this concept have been published.
|