Multilayer Self-organizing Map Research Articles

Integrated analysis of -omic landscapes in breast cancer subtypes

The subtypes of breast cancer exhibit diverse histology, molecular features, therapeutic response, aggressiveness, and patient outcomes. Multi-omics high-throughput technologies, which are widely used in cancer research, generated waste amounts of multimodal omic datasets calling for new approaches of integrated analyses to uncover patterns of transcriptomic, genomic, and epigenetic changes in breast cancer subtypes and connect them to disease clinical characteristics. Here, we applied multi-layer self-organizing map (ml-SOM) algorithms to PAM50-classified TCGA breast cancer samples to disentangle the diversity of the effects of gene expression, methylation, copy number, and somatic single nucleotide variation in the disease subtypes. Furthermore, we studied the association of perturbed gene modules with survival, prognosis, and other clinical characteristics. Our findings highlight the power of multi-omic analyses to offer a better understanding of the molecular diversity of breast cancer subtypes compared to single-omic analyses. Moreover, they highlight the complex subtype-characteristic associations between gene expression and epigenetic/genomic factors and their implications for survival and clinical outcomes.

Identifying green citizen typologies by mining household-level survey data

Some impactful but unfavorable results of rapid urbanization are human-nature disconnection, waste of energy/water resources, and increased greenhouse gas emissions. To save the future of our planet, a transition to a more sustainable urban life is a must. However, there is no single sustainable city model because cities differ in terms of their assets. Hence, locally customized sustainable actions linked to global sustainability should be developed, such as a change in individual behaviors leads to a sustainable society, city, and country. This research investigated green citizen profiles and variables affecting the profiles in the context of environmental behavior and sustainability. For this purpose, survey research was done at the household level in a metropolis in Turkey. Measurement scales about environmental concern, human-nature connections, and sustainable consumption behavior were used for collecting data. A data analysis approach was proposed as the survey dataset contains mixed-type variables. It amalgamates statistics with machine learning algorithms, namely two-stage clustering with multilayered self-organizing maps, k-medoid clustering algorithm, factor analysis, permutational multivariate analysis of variance, principal component analysis and classification and regression trees algorithm. The results reveal that (i) five distinct profiles, namely unconscious greens, risky greens, economic greens, potential greens, and wasters are identified, none of which is entirely green; (ii) district, family life-cycle, household size, number of rooms, altruistic and biocentric environmental concerns are the most critical variables in distinguishing profiles; (iii) the proposed approach enables processing socio-demographic, psychographic, behavioral and consumption variables together.

Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.

Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers.

Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype “portrayal” with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.

Transcriptome-Guided Drug Repositioning.

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies “drug target” spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs’ action for approved diseases (ulcerative colitis and Crohn’s disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn’s disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.

DPLS-MSOM Modeling for Visual Industrial Fault Diagnosis and Monitoring Based on Variation Data from Normal to Anomalous

Process variables obtain significant dynamic variation characteristics from the moment faults are introduced through a continuous period of time. Therefore, these data possess not only strong corresponding fault information but also robust dynamic characteristics that can be effectively used for fault diagnosis. Accordingly, the dynamic partial least squares (DPLS) and self-organizing map (SOM) combination methodology is utilized for visual fault diagnosis and monitoring. Different types of fault data (from normal to anomalous) are collected and trained by DPLS in the form of a dynamic data matrix to reveal the most discriminated orientations for the different statuses. A multilayer SOM is then trained to model the relationship with different status data under such orientations to the regular positions on a two-dimensional map. The trained map can be used for deciding the real operating state of the online observations. Excellent experimental results on the Tennessee Eastman chemical process have demonstr...

A Robust Approach for the Background Subtraction Based on Multi-Layered Self-Organizing Maps.

Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization. According to the winner-take-all rule, each layer of the network models an independent state of the background scene, in response to external disturbing conditions, such as illumination variations, moving backgrounds, and jittering. As a result, our background subtraction method exhibits high generalization capabilities that in combination with a post-processing filtering schema allow to produce accurate motion segmentation. Moreover, we propose an approach to detect anomalous events (such as camera motion) that require background model re-initialization. We describe our method in full details and we compare it against the most recent background subtraction approaches. Experimental results for video sequences from the 2012 and 2014 CVPR Change Detection data sets demonstrate how our methodology outperforms many state-of-the-art methods in terms of detection rate.

Organizing Books and Authors by Multilayer SOM

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.

Stacked Multilayer Self-Organizing Map for Background Modeling

In this paper, a new background modeling method called stacked multilayer self-organizing map background model (SMSOM-BM) is proposed, which presents several merits such as strong representative ability for complex scenarios, easy to use, and so on. In order to enhance the representative ability of the background model and make the parameters learned automatically, the recently developed idea of representative learning (or deep learning) is elegantly employed to extend the existing single-layer self-organizing map background model to a multilayer one (namely, the proposed SMSOM-BM). As a consequence, the SMSOM-BM gains several merits including strong representative ability to learn background model of challenging scenarios, and automatic determination for most network parameters. More specifically, every pixel is modeled by a SMSOM, and spatial consistency is considered at each layer. By introducing a novel over-layer filtering process, we can train the background model layer by layer in an efficient manner. Furthermore, for real-time performance consideration, we have implemented the proposed method using NVIDIA CUDA platform. Comparative experimental results show superior performance of the proposed approach.

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg–Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.

Research of Artificial Neural Networks Abilities in Printed Words Recognition

Research of Artificial Neural Networks Abilities in Printed Words Recognition This paper provides a brief overview on document analysis and recognition area, highlighting main steps and modules that are used to build recognition systems of the mentioned type. We underline basic workflow of such system down to the problem of single character recognition problem and highlighting possibilities and ways for artificial neural networks usage. Further we are conducting a formal comparison of abilities of printed characters recognition between two well known types of second generation neural networks, namely feedforward back-propagation multilayer perceptron (MLP) and Kohonen self-organizing features map (SOM).

Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection

This paper proposes a new document retrieval (DR) and plagiarism detection (PD) system using multilayer self-organizing map (MLSOM). A document is modeled by a rich tree-structured representation, and a SOM-based system is used as a computationally effective solution. Instead of relying on keywords/lines, the proposed scheme compares a full document as a query for performing retrieval and PD. The tree-structured representation hierarchically includes document features as document, pages, and paragraphs. Thus, it can reflect underlying context that is difficult to acquire from the currently used word-frequency information. We show that the tree-structured data is effective for DR and PD. To handle tree-structured representation in an efficient way, we use an MLSOM algorithm, which was previously developed by the authors for the application of image retrieval. In this study, it serves as an effective clustering algorithm. Using the MLSOM, local matching techniques are developed for comparing text documents. Two novel MLSOM-based PD methods are proposed. Detailed simulations are conducted and the experimental results corroborate that the proposed approach is computationally efficient and accurate for DR and PD.

Acquisition of aspect in self-organizing connectionist models

Two connectionist networks, DISLEX and DevLex-II, were used in this study to model the acquisition of lexical and grammatical aspect. Both models use multi-layered self-organizing feature maps, connected by associative links trained according to the Hebbian learning rule. Previous empirical research has identified a strong association between lexical aspect and grammatical aspect in child language, on the basis of which some researchers argue for innate semantic categories or prelinguistic predispositions. Our simulations indicate that such an association can emerge from dynamic self-organization and Hebbian learning in connectionist networks, without the need of a priori assumptions about the structure of innate knowledge. Our modeling results further attest to the utility of self-organizing neural networks in the study of language acquisition.

A flexible multi-layer self-organizing map for generic processing of tree-structured data

A new multi-layer self-organizing map (MLSOM) is proposed for unsupervised processing tree-structured data. The MLSOM is an improved self-organizing map for handling structured data. By introducing multiple SOM layers, the MLSOM can overcome the computational speed and visualization problems of SOM for structured data (SOM-SD). Node data in different levels of a tree are processed in different layers of the MLSOM. Root nodes are dedicatedly processed on the top SOM layer enabling the MLSOM a better utilization of SOM map compared with the SOM-SD. Thus, the MLSOM exhibits better data organization, clustering, visualization, and classification results of tree-structured data. Experimental results on three different data sets demonstrate that the proposed MLSOM approach can be more efficient and effective than the SOM-SD.

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.

Dynamic perception after visually guided grasping by a human-like autonomous robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.

Distortion tolerant pattern recognition based on self-organizing feature extraction

A generic, modular, neural network-based feature extraction and pattern classification system is proposed for finding essentially two-dimensional objects or object parts from digital images in a distortion tolerant manner, The distortion tolerance is built up gradually by successive blocks in a pipeline architecture. The system consists of only feedforward neural networks, allowing efficient parallel implementation. The most time and data-consuming stage, learning the relevant features, is wholly unsupervised and can be made off-line. The consequent supervised stage where the object classes are learned is simple and fast. The feature extraction is based on distortion tolerant Gabor transformations, followed by minimum distortion clustering by multilayer self-organizing maps. Due to the unsupervised learning strategy, there is no need for preclassified training samples or other explicit selection for training patterns during the training, which allows a large amount of training material to be used at the early stages, A supervised, one-layer subspace network classifier on top of the feature extractor is used for object labeling. The system has been trained with natural images giving the relevant features, and human faces and their parts have been used as the object classes for testing. The current experiments indicate that the feature space has sufficient resolution power for a moderate number of classes with rather strong distortions.

A multilayer self-organizing feature map for range image segmentation

This paper proposes and describes a hierarchical self-organizing neural network for range image segmentation. The multilayer self-organizing feature map (MLSOFM), which is an extension of the traditional (single-layer) self-organizing feature map (SOFM) is seen to alleviate the shortcomings of the latter in the context of range image segmentation. The problem of range image segmentation is formulated as one of vector quantization and is mapped onto the MLSOFM. The MLSOFM combines the ideas of self-organization and topographic mapping with those of multiscale image segmentation. Experimental results using real range images are presented.

多層構造を有する自己組織化セマンティックマップ

多層構造を有する自己組織化セマンティックマップ