scholarly journals Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Claudio Durán ◽  
Sara Ciucci ◽  
Alessandra Palladini ◽  
Umer Z. Ijaz ◽  
Antonio G. Zippo ◽  
...  
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Dynamic Balance ◽  
Reduction Techniques ◽  
Differential Network ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
H Pylori ◽  
Multivariate Pattern

AbstractThe stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.

scholarly journals Machine learning pattern recognition and differential network analysis of gastric microbiome in the presence of proton pump inhibitor treatment or Helicobacter pylori infection

2020 ◽  
Author(s):  
Sara Ciucci ◽  
Claudio Durán ◽  
Alessandra Palladini ◽  
Umer Z. Ijaz ◽  
Francesco Paroni Sterbini ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Network Analysis ◽  
Proton Pump ◽  
Dynamic Balance ◽  
Principal Component ◽  
Differential Network Analysis ◽  
Reduction Techniques ◽  
Differential Network ◽  
Linear Dimensionality Reduction ◽  
H Pylori

AbstractAlthough long thought to be a sterile and inhospitable environment, the stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of orally administered drugs such as Proton Pump Inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in the early phase. They mainly focus on the most prevalent taxa and rely on linear techniques for multivariate analysis.Here we disclose the importance of complementing linear dimensionality reduction techniques such as Principal Component Analysis and Multidimensional Scaling with nonlinear approaches derived from the physics of complex systems. Then, we show the importance to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of re-organizations which emerge from combinatorial microbial variations induced by a medical treatment (PPIs) or an infectious state (H. pylori).

scholarly journals Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 991 ◽  
Author(s):  
Md Hasan ◽  
Jong-Myon Kim
Keyword(s):  
Fault Detection ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Reduction Techniques ◽  
Spherical Tank ◽  
Frequency Domains ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
Ae Signals

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.

Non-Linear Dimensionality Reduction Techniques

2011 ◽  
pp. 1416-1424 ◽  
Author(s):  
Dilip Kumar Pratihar
Keyword(s):  
Mathematical Modeling ◽  
Dimensionality Reduction ◽  
Three Dimensions ◽  
Lower Dimension ◽  
World Systems ◽  
Reduction Techniques ◽  
Higher Dimensional ◽  
Dimensionality Reduction Techniques ◽  
Computationally Expensive ◽  
Linear Dimensionality Reduction

Most of the complex real-world systems involve more than three dimensions and it may be difficult to model these higher dimensional data related to their inputoutput relationships, mathematically. Moreover, the mathematical modeling may become computationally expensive for the said systems. A human being can visualize only up to three dimensions (3-D). So, any system involving more than 3-D cannot be visualized. To overcome this difficulty, higher dimensional data are generally mapped into either 2-D or 3-D, for visualization and ease of modeling. Dimensionality reduction techniques are nothing but the mapping methods, with the help of which the higher dimensional data can be mapped into the lower dimension after ensuring a reasonable accuracy. It is to be noted that the precision of modeling depends on the said accuracy in mapping. Thus, it is worthy to study the dimensionality reduction techniques.

scholarly journals Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Akram Vasighizaker ◽  
Saiteja Danda ◽  
Luis Rueda
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Reduction Techniques ◽  
Non Linear ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
The Impact

AbstractIdentifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

scholarly journals Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics

2020 ◽  
Vol 8 ◽  
Author(s):  
Alexandra A. Portnova-Fahreeva ◽  
Fabio Rizzoglio ◽  
Ilana Nisky ◽  
Maura Casadio ◽  
Ferdinando A. Mussa-Ivaldi ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Hand Kinematics ◽  
Reduction Techniques ◽  
Non Linear ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction
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