(PDF) Figure 1: Clustering of the HCV Samples Into

Figure 1: Clustering of the HCV Samples Into Genotypes; The First Bar of Colors Corresponds to the Genotypes Currently Acknowledged by ICTV, the Second to the mPTP ML Clustering and the Third to the ABGD Clustering (P = 0.0599, X = 1.5).

doi 10.7717/peerj.7754/fig-1

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Figure 3: Clustering of the HBV Samples Into Genotypes; The First Colored Bar Corresponds to the Genotypes Currently Acknowledged by ICTV, the Second to the mPTP ML Clustering and the Third to the ABGD Clustering (P = 0.0129, X = 0.5).

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Figure 1: Clustering of the HCV Samples Into Genotypes; The First Bar of Colors Corresponds to the Genotypes Currently Acknowledged by ICTV, the Second to the mPTP ML Clustering and the Third to the ABGD Clustering (P = 0.0599, X = 1.5).

doi 10.7717/peerj.7754/fig-1

Full Text

Abstract

Date

Authors

Publisher

Related search

Figure 3: Clustering of the HBV Samples Into Genotypes; The First Colored Bar Corresponds to the Genotypes Currently Acknowledged by ICTV, the Second to the mPTP ML Clustering and the Third to the ABGD Clustering (P = 0.0129, X = 0.5).

Using Hierarchical Clustering and Dendrograms to Quantify the Clustering of Membrane Proteins

Figure 2: A Comparison of the Clustering Performance Between Local Clustering and Global Clustering.

Figure 4: Hierarchical Clustering of the Cognitive Features.

A Method to Accelerate Human in the Loop Clustering

From Ensemble Clustering to Multi-View Clustering

Comparative Study Between the Proposed GA Based ISODAT Clustering and the Conventional Clustering Methods

Action-Space Clustering of Tidal Streams to Map the Galactic Potential

A Clustering Approach to Reduce the Available Bandwidth Estimation Error