Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.

Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in determining the root causes of abnormal processes. In previous studies, data augmentation-based deep learning (DL) techniques were most commonly used for the identification of wafer map defect patterns (WMDP). Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations were manually designed for the WMDP problem. In this study, we propose a DL-based method with automatic data augmentation for the WMDP task. Basically, it focuses on learning effective discriminative features, from wafer maps, through a deep network structure. The network consists of a convolution-based variational autoencoder (CVAE) sequentially. First, we pre-trained the CVAE on large training data in an unsupervised manner. Second, we fine-tuned the encoder of the CVAE, which was followed by a neural network (NN) classifier, in a supervised manner. Additionally, we describe a simple procedure for automatically searching for improved data augmentation policies. The policy mainly consists of five image processing functions: rotation, flipping, shifting, shearing range, and zooming. The effectiveness of the proposed method was demonstrated through experimental results obtained from a simulation dataset and a real-world wafer map dataset (WM-811K). This study provides guidance for the application of deep learning in semiconductor manufacturing processes to improve product quality and yield.

In this study, an integrated deep learning framework was developed for classifying the periodontitis stages of each individual tooth using dental panoramic radiographs. Based on actual patient panoramic radiographs data, the bone loss by periodontitis and cementoenamel junction boundaries were detected, while the tooth number and tooth length were identified using data from AIHub, an open database platform. The two factors were integrated to classify and to evaluate the periodontitis staging on dental panoramic radiography. Periodontitis is classified into four stages based on the criteria of the radiographic bone level, as suggested at the relevant international conference in 2017. For the integrated deep learning framework developed in this study, the classification performance was evaluated by comparing the results of dental specialists, which indicated that the integrated framework had an accuracy of 0.929, with a recall and precision of 0.807 and 0.724, respectively, in average across all four stages. The novel framework was thus shown to exhibit a relatively high level of performance, and the findings in this study are expected to assist dental specialists with detecting the periodontitis stage and subsequent effective treatment. A systematic application will be developed in the future, to provide ancillary data for diagnosis and basic data for the treatment and prevention of periodontal disease.

Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis.

Amid growing concern over the changing climate, environment, and health care, the interconnectivity between cardiovascular diseases, coupled with rapid industrialization, and a variety of environmental factors, has been the focus of recent research. It is necessary to research risk factor extraction techniques that consider individual external factors and predict diseases and conditions. Therefore, we designed a framework to collect and store various domains of data on the causes of cardiovascular disease, and constructed a big data integrated database. A variety of open source databases were integrated and migrated onto distributed storage devices. The integrated database was composed of clinical data on cardiovascular diseases, national health and nutrition examination surveys, statistical geographic information, population and housing censuses, meteorological administration data, and Health Insurance Review and Assessment Service data. The framework was composed of data, speed, analysis, and service layers, all stored on distributed storage devices. Finally, we proposed a framework for a cardiovascular disease prediction system based on lambda architecture to solve the problems associated with the real-time analyses of big data. This system can be used to help predict and diagnose illnesses, such as cardiovascular diseases.