scholarly journals MIC: Mutual Information Based Hierarchical Clustering

2008 ◽  
pp. 101-123 ◽  
Author(s):  
Alexander Kraskov ◽  
Peter Grassberger
Keyword(s):  
Mutual Information ◽  
Hierarchical Clustering

scholarly journals Integrative statistical analyses of multiple liquid biopsy analytes in metastatic breast cancer

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Corinna Keup ◽  
Vinay Suryaprakash ◽  
Siegfried Hauch ◽  
Markus Storbeck ◽  
Peter Hahn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Metastatic Breast Cancer ◽  
Mutual Information ◽  
Hierarchical Clustering ◽  
Liquid Biopsy ◽  
Metastatic Breast ◽  
The Other ◽  
Theoretic Analysis ◽  
Graph Theoretic

Abstract Background Single liquid biopsy analytes (LBAs) have been utilized for therapy selection in metastatic breast cancer (MBC). We performed integrative statistical analyses to examine the clinical relevance of using multiple LBAs: matched circulating tumor cell (CTC) mRNA, CTC genomic DNA (gDNA), extracellular vesicle (EV) mRNA, and cell-free DNA (cfDNA). Methods Blood was drawn from 26 hormone receptor-positive, HER2-negative MBC patients. CTC mRNA and EV mRNA were analyzed using a multi-marker qPCR. Plasma from CTC-depleted blood was utilized for cfDNA isolation. gDNA from CTCs was isolated from mRNA-depleted CTC lysates. CTC gDNA and cfDNA were analyzed by targeted sequencing. Hierarchical clustering was performed within each analyte, and its results were combined into a score termed Evaluation of multiple Liquid biopsy analytes In Metastatic breast cancer patients All from one blood sample (ELIMA.score), which calculates the contribution of each analyte to the overall survival prediction. Singular value decomposition (SVD), mutual information calculation, k-means clustering, and graph-theoretic analysis were conducted to elucidate the dependence between individual analytes. Results A combination of two/three/four LBAs increased the prevalence of patients with actionable signals. Aggregating the results of hierarchical clustering of individual LBAs into the ELIMA.score resulted in a highly significant correlation with overall survival, thereby bolstering evidence for the additive value of using multiple LBAs. Computation of mutual information indicated that none of the LBAs is independent of the others, but the ability of a single LBA to describe the others is rather limited—only CTC gDNA could partially describe the other three LBAs. SVD revealed that the strongest singular vectors originate from all four LBAs, but a majority originated from CTC gDNA. After k-means clustering of patients based on parameters of all four LBAs, the graph-theoretic analysis revealed CTC ERBB2 variants only in patients belonging to one particular cluster. Conclusions The additional benefits of using all four LBAs were objectively demonstrated in this pilot study, which also indicated a relative dominance of CTC gDNA over the other LBAs. Consequently, a multi-parametric liquid biopsy approach deconvolutes the genomic and transcriptomic complexity and should be considered in clinical practice.

scholarly journals A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0137278 ◽  
Author(s):  
Guillaume Marrelec ◽  
Arnaud Messé ◽  
Pierre Bellec
Keyword(s):  
Mutual Information ◽  
Hierarchical Clustering ◽  
Random Variables ◽  
Dependent Random Variables

scholarly journals Hierarchical clustering using mutual information

2005 ◽  
Vol 70 (2) ◽  
pp. 278-284 ◽  
Author(s):  
A Kraskov ◽  
H Stögbauer ◽  
R. G Andrzejak ◽  
P Grassberger
Keyword(s):  
Mutual Information ◽  
Hierarchical Clustering

The maximum mutual information without coding for binary quantum-state signals

1998 ◽  
Vol 45 (2) ◽  
pp. 269-282 ◽  
Author(s):  
MASAO OSAKI, OSAMU HIROTA MASASHI BAN
Keyword(s):  
Mutual Information ◽  
Quantum State ◽  
Maximum Mutual Information

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function

Nonlinear Pattern Analysis of Ventricular Premature Beats by Mutual Information

1997 ◽  
Vol 36 (04/05) ◽  
pp. 257-260 ◽  
Author(s):  
H. Saitoh ◽  
T. Yokoshima ◽  
H. Kishida ◽  
H. Hayakawa ◽  
R. J. Cohen ◽  
...  
Keyword(s):  
Mutual Information ◽  
Temporal Pattern ◽  
Pattern Analysis ◽  
Autonomic Activity ◽  
Correlation Measure ◽  
Ventricular Premature Beats ◽  
Risk Of Mortality ◽  
Premature Beats ◽  
Information Values ◽  
Nonlinear Pattern

Abstract:The frequency of ventricular premature beats (VPBs) has been related to the risk of mortality. However, little is known about the temporal pattern of occurrence of VPBs and its relationship to autonomic activity. Hence, we applied a general correlation measure, mutual information, to quantify how VPBs are generated over time. We also used mutual information to determine the correlation between VPB production and heart rate in order to evaluate effects of autonomic activity on VPB production. We examined twenty subjects with more than 3000 VPBs/day and simulated ran-( dom time series of VPB occurrence. We found that mutual information values could be used to characterize quantitatively the temporal patterns of VPB generation. Our data suggest that VPB production is not random and VPBs generated with a higher value of mutual information may be more greatly affected by autonomic activity.

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