scholarly journals Clustering of the structures by using “snakes-&-dragons” approach, or correlation matrix as a signal

2019 ◽  
Author(s):  
Victor P. Andreev ◽  
Gang Liu ◽  
Jarcy Zee ◽  
Lisa Henn ◽  
Gilberto E. Flores ◽  
...  
Keyword(s):  
Correlation Matrix ◽  
Human Microbiome ◽  
Complex Structures ◽  
Technological Systems ◽  
Correlation Matrices ◽  
Multiple Networks ◽  
Novel Approach ◽  
System States

AbstractBiological, ecological, social, and technological systems are complex structures with multiple interacting parts, often represented by networks. Correlation matrices describing interdependency of the variables in such structures provide key information for comparison and classification of such systems. Classification based on correlation matrices could supplement or improve classification based on variable values, since the former reveals similarities in system structures, while the latter relies on the similarities in system states. Importantly, this approach of clustering correlation matrices is different from clustering elements of the correlation matrices, because our goal is to compare and cluster multiple networks – not the nodes within the networks. A novel approach for clustering correlation matrices, named “snakes-&-dragons,” is introduced and illustrated by examples from neuroscience, human microbiome, and macroeconomics.

scholarly journals A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study

2021 ◽  
Vol 5 (3) ◽  
pp. 39
Author(s):  
Bhargav Prakash ◽  
Gautam Kumar Baboo ◽  
Veeky Baths
Keyword(s):  
Correlation Matrix ◽  
Brain Connectivity ◽  
Support Vector ◽  
Emotional States ◽  
Learning Models ◽  
Time Resolved ◽  
Correlation Matrices ◽  
Novel Approach ◽  
Lower Accuracy ◽  
Eeg Data

Brain connectivity is studied as a functionally connected network using statistical methods such as measuring correlation or covariance. The non-invasive neuroimaging techniques such as Electroencephalography (EEG) signals are converted to networks by transforming the signals into a Correlation Matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Networks (RNN), are implemented on two different types of correlation matrices: Correlation Matrix (static connectivity) and Time-resolved Correlation Matrix (dynamic connectivity), to classify them either on their psychometric assessment or the effect of therapy. These correlation matrices are different from traditional learning techniques in the sense that they incorporate theory-based graph features into the learning models, thus providing novelty to this study. The EEG data used in this study is trail-based/event-related from five different experimental paradigms, of which can be broadly classified as working memory tasks and assessment of emotional states (depression, anxiety, and stress). The classifications based on RNN provided higher accuracy (74–88%) than the other three models (50–78%). Instead of using individual graph features, a Correlation Matrix provides an initial test of the data. When compared with the Time-resolved Correlation Matrix, it offered a 4–5% higher accuracy. The Time-resolved Correlation Matrix is better suited for dynamic studies here; it provides lower accuracy when compared to the Correlation Matrix, a static feature.

scholarly journals Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data

2021 ◽  
Author(s):  
David Lewis-Smith ◽  
Shiva Ganesan ◽  
Peter D. Galer ◽  
Katherine L. Helbig ◽  
Sarah E. McKeown ◽  
...  
Keyword(s):  
Clinical Data ◽  
Similarity Analysis ◽  
Genetic Studies ◽  
Human Phenotype ◽  
Phenotypic Similarity ◽  
Complex Phenotypes ◽  
Cognitive Framework ◽  
Novel Approach ◽  
Genetic Epilepsies

AbstractWhile genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

scholarly journals A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carlo Donadio ◽  
Massimo Brescia ◽  
Alessia Riccardo ◽  
Giuseppe Angora ◽  
Michele Delli Veneri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Morphological Variations ◽  
Control Factors ◽  
Novel Approach ◽  
Drainage Networks ◽  
Key Characteristics ◽  
Intrinsic Complexity ◽  
Solar System Bodies ◽  
Manual Extraction

AbstractSeveral approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

Advances in the Biology and Genetics of the Podocytopathies: Implications for Diagnosis and Therapy

2009 ◽  
Vol 133 (2) ◽  
pp. 201-216 ◽  
Author(s):  
Laura Barisoni ◽  
H. William Schnaper ◽  
Jeffrey B. Kopp
Keyword(s):  
Current Knowledge ◽  
Diffuse Mesangial Sclerosis ◽  
Therapeutic Approaches ◽  
Glomerular Diseases ◽  
Podocyte Injury ◽  
Collapsing Glomerulopathy ◽  
Novel Approach ◽  
Genes Encoding ◽  
Glomerular Morphology

AbstractContext.—Etiologic factors and pathways leading to altered podocyte phenotype are clearly numerous and involve the activity of different cellular function.Objective.—To focus on recent discoveries in podocyte biology and genetics and their relevance to these human glomerular diseases, named podocytopathies.Data Sources.—Genetic mutations in genes encoding for proteins in the nucleus, slit diaphragm, podocyte cytoplasm, and cell membrane are responsible for podocyte phenotype and functional abnormalities. Podocyte injury may also derive from secondary stimuli, such as mechanical stress, infections, or use of certain medications. Podocytes can respond to injury in a limited number of ways, which include (1) effacement, (2) apoptosis, (3) arrest of development, and (4) dedifferentiation. Each of these pathways results in a specific glomerular morphology: minimal change nephropathy, focal segmental glomerulosclerosis, diffuse mesangial sclerosis, and collapsing glomerulopathy.Conclusions.—Based on current knowledge of podocyte biology, we organized etiologic factors and morphologic features in a taxonomy of podocytopathies, which provides a novel approach to the classification of these diseases. Current and experimental therapeutic approaches are also discussed.

scholarly journals Using correlation matrices in predicting airline performance

2021 ◽  
pp. 15-18
Author(s):  
O. G. Feoktistova ◽  
◽  
D. Yu. Potapova ◽  
Keyword(s):  
Life Cycle ◽  
Forecast Accuracy ◽  
Air Transportation ◽  
Correlation Matrices ◽  
Effective Functioning ◽  
Forecasting Method ◽  
Airline Performance

The article considers the issue of making forecasts of the main indicators of the activities of airlines for their effective functioning in the air transportation market. It describes the life cycle of airlines and provides a classification of the forecasts that are currently being made. The interrelation of correlations of the key parameters of the airline’s functioning is considered, the use of correlations in forecasting is shown, actual calculations are presented, and a significant increase in forecast accuracy when using this forecasting method is demonstrated.

USTC-MUSIC Algorithm for DOA Estimation of Coherent Sources Using Single Vector Hydrophone

2014 ◽  
Vol 530-531 ◽  
pp. 530-533
Author(s):  
Jin Fang Cheng ◽  
Chao Ran Zhang ◽  
Wei Zhang
Keyword(s):  
Correlation Matrix ◽  
Temporal Correlation ◽  
Doa Estimation ◽  
Spatial Smoothing ◽  
Music Algorithm ◽  
Correlation Matrices ◽  
Vector Hydrophone ◽  
Coherent Sources ◽  
Simulation Results ◽  
Spatio Temporal

The MUSIC algorithm cannot deal with the problem of DOA estimation of coherent sources, this paper proposes the USTC (unitary spatio-temporal correlation matrices)-MUSIC algorithm using single vector hydrophone to solve this problem, by utilizing the unitary spatio-temporal correlation matrix instead of the covariance matrix. The simulation results demonstrate that the USTC-MUSIC algorithm has a better ability to distinguish the coherent sources from different directions than the spatial smoothing MUSIC algorithm.

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