Simultaneous Visualization and Clustering Methods as an Alternative to Kohonen Maps

1997 ◽  
pp. 67-85 ◽  
Author(s):  
H. H. Bock
Keyword(s):  
Clustering Methods ◽  
Kohonen Maps ◽  
Simultaneous Visualization

Three-Dimensional Subcellular Organization of Hepatocytes in Intact Liver: Simultaneous Visualization of Cytoskeletal and Membrane Markers by Confocal Microscopy

1996 ◽  
Vol 54 ◽  
pp. 288-289
Author(s):  
Greg V. Martin ◽  
Ann L. Hubbard
Keyword(s):  
Three Dimensional ◽  
Integral Membrane Proteins ◽  
Polarized Secretion ◽  
Microscope Images ◽  
Computer Aided ◽  
Intracellular Organelles ◽  
Cytoskeletal Elements ◽  
Simultaneous Visualization

The microtubule (MT) cytoskeleton is necessary for many of the polarized functions of hepatocytes. Among the functions dependent on the MT-based cytoskeleton are polarized secretion of proteins, delivery of endocytosed material to lysosomes, and transcytosis of integral plasma membrane (PM) proteins. Although microtubules have been shown to be crucial to the establishment and maintenance of functional and structural polarization in the hepatocyte, little is known about the architecture of the hepatocyte MT cytoskeleton in vivo, particularly with regard to its relationship to PM domains and membranous organelles. Using an in situ extraction technique that preserves both microtubules and cellular membranes, we have developed a protocol for immunofluorescent co-localization of cytoskeletal elements and integral membrane proteins within 20 µm cryosections of fixed rat liver. Computer-aided 3D reconstruction of multi-spectral confocal microscope images was used to visualize the spatial relationships among the MT cytoskeleton, PM domains and intracellular organelles.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

2018 ◽  
Vol 18 (13) ◽  
pp. 1110-1122 ◽  
Author(s):  
Juan F. Morales ◽  
Lucas N. Alberca ◽  
Sara Chuguransky ◽  
Mauricio E. Di Ianni ◽  
Alan Talevi ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Topological Descriptors ◽  
Log P ◽  
Acidity Constant ◽  
Molecular Topology ◽  
Clustering Methods ◽  
Mean Values ◽  
Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

scholarly journals Simultaneous Visualization of 161Tb- and 177Lu-Labeled Somatostatin Analogues Using Dual-Isotope SPECT Imaging

Pharmaceutics ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 536
Author(s):  
Francesca Borgna ◽  
Patrick Barritt ◽  
Pascal V. Grundler ◽  
Zeynep Talip ◽  
Susan Cohrs ◽  
...  
Keyword(s):  
Pharmacokinetic Profile ◽  
Somatostatin Analogues ◽  
Distribution Coefficients ◽  
Spect Imaging ◽  
Cell Uptake ◽  
Organ Distribution ◽  
Dual Isotope ◽  
Biodistribution Studies ◽  
Simultaneous Visualization

The decay of terbium-161 results in the emission of β¯-particles as well as conversion and Auger electrons, which makes terbium-161 interesting for therapeutic purposes. The aim of this study was to use dual-isotope SPECT imaging in order to demonstrate visually that terbium-161 and lutetium-177 are interchangeable without compromising the pharmacokinetic profile of the radiopharmaceutical. The 161Tb- and 177Lu-labeled somatostatin (SST) analogues DOTATOC (agonist) and DOTA-LM3 (antagonist) were tested in vitro to demonstrate equal properties regarding distribution coefficients and cell uptake into SST receptor-positive AR42J tumor cells. The radiopeptides were further investigated in AR42J tumor-bearing nude mice using the method of dual-isotope (terbium-161/lutetium-177) SPECT/CT imaging to enable the visualization of their distribution profiles in the same animal. Equal pharmacokinetic profiles were demonstrated for either of the two peptides, irrespective of whether it was labeled with terbium-161 or lutetium-177. Moreover, the visualization of the sub-organ distribution confirmed similar behavior of 161Tb- and 177Lu-labeled SST analogues. The data were verified in quantitative biodistribution studies using either type of peptide labeled with terbium-161 or lutetium-177. While the radionuclide did not have an impact on the organ distribution, this study confirmed previous data of a considerably higher tumor uptake of radiolabeled DOTA-LM3 as compared to the radiolabeled DOTATOC.

scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

scholarly journals Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregoire Preud’homme ◽  
Kevin Duarte ◽  
Kevin Dalleau ◽  
Claire Lacomblez ◽  
Emmanuel Bresso ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Latent Class ◽  
Latent Class Model ◽  
Real Life ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Clustering Methods ◽  
Model Based ◽  
Partitioning Around Medoids

AbstractThe choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

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