scholarly journals Union Coding Based Immune Clone Selection Unsupervised Clustering Algorithm

2012 ◽  
Author(s):  
Lichao Mou ◽  
Xiaoying Pan
Keyword(s):  
Clustering Algorithm ◽  
Unsupervised Clustering ◽  
Clone Selection ◽  
Immune Clone Selection

462 Redefining Positive Airway Pressure Adherence Phenotypes Utilizing Deep Neural Networks and Unsupervised Clustering

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A182-A182
Author(s):  
Yoav Nygate ◽  
Sam Rusk ◽  
Chris Fernandez ◽  
Nick Glattard ◽  
Nathaniel Watson ◽  
...  
Keyword(s):  
Airway Pressure ◽  
Clustering Algorithm ◽  
Positive Airway Pressure ◽  
Treatment Success ◽  
Unsupervised Clustering ◽  
Additional Patient ◽  
Apnea Hypopnea Index ◽  
Obstructive Sleep ◽  
Usage Patterns ◽  
Usage Data

Abstract Introduction Improving positive airway pressure (PAP) adherence is crucial to obstructive sleep apnea (OSA) treatment success. We have previously shown the potential of utilizing Deep Neural Network (DNN) models to accurately predict future PAP usage, based on predefined compliance phenotypes, to enable early patient outreach and interventions. These phenotypes were limited, based solely on usage patterns. We propose an unsupervised learning methodology for redefining these adherence phenotypes in order to assist with the creation of more precise and personalized patient categorization. Methods We trained a DNN model to predict PAP compliance based on daily usage patterns, where compliance was defined as the requirement for 4 hours of PAP usage a night on over 70% of the recorded nights. The DNN model was trained on N=14,000 patients with 455 days of daily PAP usage data. The latent dimension of the trained DNN model was used as a feature vector containing rich usage pattern information content associated with overall PAP compliance. Along with the 455 days of daily PAP usage data, our dataset included additional patient demographics such as age, sex, apnea-hypopnea index, and BMI. These parameters, along with the extracted usage patterns, were applied together as inputs to an unsupervised clustering algorithm. The clusters that emerged from the algorithm were then used as indicators for new PAP compliance phenotypes. Results Two main clusters emerged: highly compliant and highly non-compliant. Furthermore, in the transition between the two main clusters, a sparse cluster of struggling patients emerged. This method allows for the continuous monitoring of patients as they transition from one cluster to the other. Conclusion In this research, we have shown that by utilizing historical PAP usage patterns along with additional patient information we can identify PAP specific adherence phenotypes. Clinically, this allows focus of PAP adherence program resources to be targeted early on to patients susceptible to treatment non-adherence. Furthermore, the transition between the two main phenotypes can also indicate when personalized intervention is necessary to maximize treatment success and outcomes. Lastly, providers can transition patients in the highly non-compliant group more quickly to alternative therapies. Support (if any):

scholarly journals Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering

2018 ◽  
Author(s):  
Mridul K. Thomas ◽  
Simone Fontana ◽  
Marta Reyes ◽  
Francesco Pomati
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Functional Groups ◽  
Clustering Algorithm ◽  
Unsupervised Clustering ◽  
Learning Tools ◽  
Time Resolved ◽  
Automated Method ◽  
Phytoplankton Communities ◽  
Cell Densities

AbstractScanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

scholarly journals Enhance Clustering Algorithm Using Optimization

2020 ◽  
Vol 3 (9) ◽  
pp. 136-142
Author(s):  
Roshankumar Ramashish Maurya ◽  
Anand Khandare
Keyword(s):  
Particle Swarm Optimization ◽  
Unsupervised Learning ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Unsupervised Clustering ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Different Types

Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.

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