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Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p

DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. Therefore, DNA sequence analysis tools play an increasingly crucial role in species identification. This study proposed deep barcoding, a deep learning framework for species classification by using DNA barcodes. Deep barcoding uses raw sequence data as the input to represent one-hot encoding as a one-dimensional image and uses a deep convolutional neural network with a fully connected deep neural network for sequence analysis. It can achieve an average accuracy of90 percent for both simulation and real datasets. Although deep learning yields outstanding performance for species classification with DNA sequences, its application remains a challenge. The deep barcoding model can be a potential tool for species classification and can elucidate DNA barcode-based species identification.

DNA barcoding is widely used in fields, such as taxonomy and species identification. Conventional DNA barcoding sequences employ uninformative or repeat nucleotides in known groups of taxa within a monophylum. Herein, we propose a decision theory-based DNA barcode that tests for the ribulose bisphosphate carboxylase gene (rbcL). The proposed method can generate shorter DNA barcodes called single nucleotide polymorphism (SNP) tags, which shorten rbcL sequences from their full length (400–654 bp) to 25-bp DNA tags. These DNA tags are then represented by quick response (QR) codes containing the species names, accession numbers, and DNA tag sequences. Our proposed method can efficiently reduce data storage and provide DNA barcoding for various plant species.

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a ribulose diphosphate carboxylase (rbcL) SNP barcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree–selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

DNA barcodes are widely used in taxonomy, systematics, species identification, food safety, and forensic science. Most of the conventional DNA barcode sequences contain the whole information of a given barcoding gene. Most of the sequence information does not vary and is uninformative for a given group of taxa within a monophylum. We suggest here a method that reduces the amount of noninformative nucleotides in a given barcoding sequence of a major taxon, like the prokaryotes, or eukaryotic animals, plants, or fungi. The actual differences in genetic sequences, called single nucleotide polymorphism (SNP) genotyping, provide a tool for developing a rapid, reliable, and high‐throughput assay for the discrimination between known species. Here, we investigated SNPs as robust markers of genetic variation for identifying different pigeon species based on available cytochrome c oxidase I (COI) data. We propose here a decision tree‐based SNP barcoding (DTSB) algorithm where SNP patterns are selected from the DNA barcoding sequence of several evolutionarily related species in order to identify a single species with pigeons as an example. This approach can make use of any established barcoding system. We here firstly used as an example the mitochondrial gene COI information of 17 pigeon species (Columbidae, Aves) using DTSB after sequence trimming and alignment. SNPs were chosen which followed the rule of decision tree and species‐specific SNP barcodes. The shortest barcode of about 11 bp was then generated for discriminating 17 pigeon species using the DTSB method. This method provides a sequence alignment and tree decision approach to parsimoniously assign a unique and shortest SNP barcode for any known species of a chosen monophyletic taxon where a barcoding sequence is available.

The purpose of gene expression analysis is to discriminate between classes of samples, and to predict the relative importance of each gene for sample classification. Microarray data with reference to gene expression profiles have provided some valuable results related to a variety of problems and contributed to advances in clinical medicine. Microarray data characteristically have a high dimension and a small sample size. This makes it difficult for a general classification method to obtain correct data for classification. However, not every gene is potentially relevant for distinguishing the sample class. Thus, in order to analyze gene expression profiles correctly, feature (gene) selection is crucial for the classification process, and an effective gene extraction method is necessary for eliminating irrelevant genes and decreasing the classification error rate. In this paper, correlation-based feature selection (CFS) and the Taguchi chaotic binary particle swarm optimization (TCBPSO) were combined into a hybrid method. The K-nearest neighbor (K-NN) with leave-one-out cross-validation (LOOCV) method served as a classifier for ten gene expression profiles. Experimental results show that this hybrid method effectively simplifies features selection by reducing the number of features needed. The classification error rate obtained by the proposed method had the lowest classification error rate for all of the ten gene expression data set problems tested. For six of the gene expression profile data sets a classification error rate of zero could be reached. The introduced method outperformed five other methods from the literature in terms of classification error rate. It could thus constitute a valuable tool for gene expression analysis in future studies.

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.

In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined in a hybrid method, and the K-nearest neighbor (KNN) method with leave-one-out cross-validation (LOOCV) served as a classifier for eleven classification profiles. With the help of this classifier classification accuracy were calculated. Experimental results show that this method effectively simplifies features selection by reducing the total number of features needed. The proposed method obtained the highest classification accuracy in five out of the six gene expression data set test problems when compared to other classification methods from the literature.

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. The gene (feature) selection can extract genes which influence classification accuracy effectively, to eliminate the useless genes, and to improve the calculate performance and the classification accuracy. This paper presents hybrid feature selection method - Taguchi-Genetic algorithm to find optimal feature subset, to appraise feature set using K-nearest neighbor with leave-one-out cross-validation based on Euclidean distance calculation. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to other classification methods from the literature.

This work presents a novel GA-Taguchi-based feature selection method. Genetic algorithms are utilized with randomness for "global search" of the entire search space of the intractable search problem. Various genetic operations, including crossover, mutation, selection and replacement are performed to assist the search procedure in escaping from sub-optimal solutions. In each iteration in the proposed nature-inspired method, the Taguchi methods are employed for "local search" of the entire search space and thus can help explore better feature subsets for next iteration. The two-level orthogonal array is utilized for a well-organized and balanced comparison of two levels for features--a feature is or is not selected for pattern classification--and interactions among features. The signal-to-noise ratio (SNR) is then used to determine the robustness of the features. As a result, feature subset evaluation efforts can be significantly reduced and a superior feature subset with high classification performance can be obtained. Experiments are performed on different application domains to demonstrate the performance of the proposed nature-inspired method. The proposed hybrid GA-Taguchi-based approach, with wrapper nature, yields superior performance and improves classification accuracy in pattern classification.