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## Cancer subtype classification and modeling by pathway attention and propagation.

#### In Bioinformatics (Oxford, England) MOTIVATION : Biological pathway is important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only 1/3 of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.RESULTS : We present an explainable deep learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. then, a multi-attention based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer data sets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.Lee Sangseon, Lim Sangsoo, Lee Taeheon, Sung Inyoung, Kim Sun2020-Mar-24

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## Deep learning based searching approach for RDF graphs.

#### In PloS one ; h5-index 176.0 The Internet is a remarkably complex technical system. Its rapid growth has also brought technical issues such as problems to information retrieval. Search engines retrieve requested information based on the provided keywords. Consequently, it is difficult to accurately find the required information without understanding the syntax and semantics of the content. Multiple approaches are proposed to resolve this problem by employing the semantic web and linked data techniques. Such approaches serialize the content using the Resource Description Framework (RDF) and execute the queries using SPARQL to resolve the problem. However, an exact match between RDF content and query structure is required. Although, it improves the keyword-based search; however, it does not provide probabilistic reasoning to find the semantic relationship between the queries and their results. From this perspective, in this paper, we propose a deep learning-based approach for searching RDF graphs. The proposed approach treats document requests as a classification problem. First, we preprocess the RDF graphs to convert them into N-Triples format. Second, bag-of-words (BOW) and word2vec feature modeling techniques are combined for a novel deep representation of RDF graphs. The attention mechanism enables the proposed approach to understand the semantic between RDF graphs. Third, we train a convolutional neural network for the accurate retrieval of RDF graphs using the deep representation. We employ 10-fold cross-validation to evaluate the proposed approach. The results show that the proposed approach is accurate and surpasses the state-of-the-art. The average accuracy, precision, recall, and f-measure are up to 97.12%, 98.17%, 95.56%, and 96.85%, respectively.Soliman Hatem2020

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## Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study.

#### In JMIR medical informatics ; h5-index 23.0 BACKGROUND : Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling.OBJECTIVE : We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan.METHODS : Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables.RESULTS : Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively.CONCLUSIONS : Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.Yu Cheng-Sheng, Lin Yu-Jiun, Lin Chang-Hsien, Wang Sen-Te, Lin Shiyng-Yu, Lin Sanders H, Wu Jenny L, Chang Shy-Shin2020-Mar-23controlled attenuation parameter technology, decision tree, machine learning, metabolic syndrome

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