Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Dual-input robust diagnostics for railway point machines via audio signals.

Railway Point Machine (RPM) is a fundamental component of railway infrastructure and plays a crucial role in ensuring the safe operation of trains. Its primary function is to divert trains from one track to another, enabling connections between different lines and facilitating route selection. By judiciously deploying turnouts, railway systems can provide efficient transportation services while ensuring the safety of passengers and cargo. As signal processing technologies develop rapidly, taking the easy acquisition advantages of audio signals, a fault diagnosis method for RPMs is proposed by considering noise and multi-channel signals. The proposed method consists of several stages. Initially, the signal is subjected to pre-processing steps, including cropping and channel separation. Subsequently, the signal undergoes noise addition using the Random Length and Dynamic Position Noises Superposition (RDS) module, followed by conversion to a greyscale image. To enhance the data, Synthetic Minority Oversampling Technique (SMOTE) module is applied. Finally, the training data is fed into a Dual-input Attention Convolutional Neural Network (DIACNN). By employing various experimental techniques and designing diverse datasets, our proposed method demonstrates excellent robustness and achieves an outstanding classification accuracy of 99.73%.

A Dilated Convolutional Neural Network for Cross-Layers of Contextual Information for Congested Crowd Counting.

Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively.

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis.

Objective.Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential OSA patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing.Approach.The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis.Main results.Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9%, a sensitivity of 85.3%, and a precision of 85.6% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7%, 10.7%, and 7.9%, respectively.Significance.The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.

Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images.

OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.

BACKGROUND: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION: Retrospectively registered.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images.

BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.

BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition. METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label. RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods. CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

A shallow mirror transformer for subject-independent motor imagery BCI.

OBJECTIVE: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI. APPROACH: In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects. MAIN RESULTS: The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification. SIGNIFICANCE: This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.

A Fault-Diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation.

In recent years, with the rapid increase in coverage and lines, security maintenance has become one of the top concerns with regard to railway transportation in China. As the key transportation infrastructure, the railway turnout system (RTS) plays a vital role in transportation, which will cause incalculable losses when accidents occur. The traditional fault-diagnosis and maintenance methods of the RTS are no longer applicable to the growing amount of data, so intelligent fault diagnosis has become a research hotspot. However, the key challenge of RTS intelligent fault diagnosis is to effectively extract the deep features in the signal and accurately identify failure modes in the face of unbalanced datasets. To solve the above two problems, this paper focuses on unbalanced data and proposes a fault-diagnosis method based on an improved autoencoder and data augmentation, which realizes deep feature extraction and fault identification of unbalanced data. An improved autoencoder is proposed to smooth the noise and extract the deep features to overcome the noise fluctuation caused by the physical characteristics of the data. Then, synthetic minority oversampling technology (SMOTE) is utilized to effectively expand the fault types and solve the problem of unbalanced datasets. Furthermore, the health state is identified by the Softmax regression model that is trained with the balanced characteristics data, which improves the diagnosis precision and generalization ability. Finally, different experiments are conducted on a real dataset based on a railway station in China, and the average diagnostic accuracy reaches 99.13% superior to other methods, which indicates the effectiveness and feasibility of the proposed method.

Railway accident prediction strategy based on ensemble learning.

Railway accident prediction is of great significance for establishing an early warning mechanism and preventing the occurrences of accidents. Safety agencies rely on prediction models to design railroad risk management strategies. Based on historical railway accident data, an ensemble learning strategy for accident prediction is proposed. Firstly, an improved K-nearest neighbors (KNN) data imputation algorithm is proposed to solve the problem of missing data in the dataset. Then, to reduce the impact of imbalanced data on prediction performance, an AdaBoost-Bagging method is presented. Finally, according to the feature importance in the prediction model, accident features are ranked to identify new insights into the cause of the accident. The AdaBoost-Bagging prediction method is applied to the Federal Railroad Administration (FRA) dataset. The application results show that, compared with Artificial Neural Network (ANN), XGBoost, GBDT, Stacking and AdaBoost methods, AdaBoost-Bagging method has a smaller prediction error and faster inference time in predicting railway accidents. Accuracy, Precision, Recall and F1-score are 0.879, 0.879, 0.883 and 0.881 respectively, and the inference time is reduced by 23.38%, 12.15%, 6.66%, 3.17% and 11.41% respectively. The prediction method can well mine important features of railway accidents without knowing the accident mechanism or the relationship between various railway accidents and factors, e.g., the critic risk factors related to derailment and collision accidents are investigated in the prediction. The findings will be helpful to the prevention and management of railway accidents.

Trusted Data Storage Architecture for National Infrastructure.

National infrastructure is a material engineering facility that provides public services for social production and residents' lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.

Transductive Semisupervised Deep Hashing.

Deep hashing methods have shown their superiority to traditional ones. However, they usually require a large amount of labeled training data for achieving high retrieval accuracies. We propose a novel transductive semisupervised deep hashing (TSSDH) method which is effective to train deep convolutional neural network (DCNN) models with both labeled and unlabeled training samples. TSSDH method consists of the following four main ingredients. First, we extend the traditional transductive learning (TL) principle to make it applicable to DCNN-based deep hashing. Second, we introduce confidence levels for unlabeled samples to reduce adverse effects from uncertain samples. Third, we employ a Gaussian likelihood loss for hash code learning to sufficiently penalize large Hamming distances for similar sample pairs. Fourth, we design the large-margin feature (LMF) regularization to make the learned features satisfy that the distances of similar sample pairs are minimized and the distances of dissimilar sample pairs are larger than a predefined margin. Comprehensive experiments show that the TSSDH method can produce superior image retrieval accuracies compared to the representative semisupervised deep hashing methods under the same number of labeled training samples.

An efficient and global interactive optimization methodology for path planning with multiple routing constraints.

Path planning problem is attracting wide attention in autonomous system and process industry system. The existed research mainly focuses on finding the shortest path from the source vertex to the termination vertex under loose constraints of vertex and edge. However, in realistic, the constraints such as specified vertexes, specified paths, forbidden paths and forbidden vertexes have to be considered, which makes the existing algorithms inefficient even infeasible. Aiming at solving the problems of complex path planning with multiple routing constraints, this paper organizes transforms the constraints into appropriate mathematical analytic expressions. Then, in order to overcome the defects of existing coding and optimization algorithms, an adaptive strategy for the vertex priority is proposed in coding, and an efficient and global optimization methodology based on swarm intelligence algorithms is put forward, which can make full use of the high efficiency of the local optimization algorithm and the high search ability of the global optimization algorithm. Moreover, the optimal convergence condition of the methodology is proved theoretically. Finally, two experiments are inducted, and the results demonstrated its efficiency and superiority.

Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system.

With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.

Multi-model train state estimation based on multi-sensor parallel fusion filtering.

Accurately determining a train's state is essential for passenger safety, operation efficiency, and maintenance. However, the actual operation state of a train is composed of a variety of modes and is disturbed by several known or unknown factors, for which an accurate estimator is required. Hence, in this paper, a train multi-mode model considering the actual operation environment is established, and a train state estimation method based on multi-sensor parallel fusion filter is proposed. In the parallel fusion filter, the current mode of train is determined by the proposed sliding window error and voting mechanism, and the global filter are constituted by the local filters, which are fused by linear-weighted summation. The simulation results demonstrate the effectiveness of our method in estimating the train's state. It is worth noting that even if monitoring data are missing or are abnormal, the state estimation accuracy of the proposed technique still meets the requirements of a real system, and the effectiveness and robustness of the method can be verified.

Computational Methods and Online Resources for Identification of piRNA-Related Molecules.

piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA-disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.

Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs.

Objective.Motor imagery (MI) EEG signals vary greatly among subjects, so scholarly research on motor imagery-based brain-computer interfaces (BCIs) has mainly focused on single-subject systems or subject-dependent systems. However, the single-subject model is applicable only to the target subject, and the small sample number greatly limits the performance of the model. This paper aims to study a convolutional neural network to achieve an adaptable MI-BCI that is applicable to multiple subjects.Approach.In this paper, a twin cascaded softmax convolutional neural network (TCSCNN) is proposed for multisubject MI-BCIs. The proposed TCSCNN is independent and can be applied to any single-subject MI classification convolutional neural network (CNN) model. First, to reduce the influence of individual differences, subject recognition and MI recognition are accomplished simultaneously. A cascaded softmax structure consisting of two softmax layers, related to subject recognition and MI recognition, is subsequently applied. Second, to improve the MI classification precision, a twin network structure is proposed on the basis of ensemble learning. TCSCNN is built by combining a cascaded softmax structure and twin network structure.Main results.Experiments were conducted on three popular CNN models (EEGNet and Shallow ConvNet and Deep ConvNet from EEGDecoding) and three public datasets (BCI Competition IV datasets 2a and 2b and the high-gamma dataset) to verify the performance of the proposed TCSCNN. The results show that compared with the state-of-the-art CNN model, the proposed TCSCNN obviously improves the precision and convergence of multisubject MI recognition.Significance.This study provides a promising scheme for multisubject MI-BCI, reflecting the progress made in the development and application of MI-BCIs.

A novel deep feature transfer-based OSA detection method using sleep sound signals.

OBJECTIVE: Polysomnography is typically used to evaluate the severity of obstructive sleep apnea (OSA) but the inconvenience of application and high cost considerably affect the diagnostics. In this study, sleep sound signals are used to detect OSA in patients. APPROACH: A deep feature transfer-based OSA detection approach is proposed. First, a deep convolutional neural network is trained on large-scale labeled audio data sets to distinguish respiration sounds from environmental noise. Second, the trained model is transferred to recognize respiration sounds in sleep sound signals. Third, the deep features of the detected respiration sounds are used to train a logistic regression classifier to identify OSA patients from potential patients. Polysomnography-based diagnosis is used as a reference. MAIN RESULTS: A self-collected data set of 132 potential OSA patients is applied in OSA detection experiments. The OSA detection performances are tested on four models for different apnea-hypopnea index thresholds and sexes resulting in accuracies of 80.17%, 80.21%, 81.63% and 77.22%. The corresponding areas under the receiver operating characteristic curves are 0.82, 0.80, 0.81 and 0.79. In addition, the proposed method presented a significant performance improvement compared with the state-of-the-art methods. SIGNIFICANCE: Big data, deep learning and transfer learning can be successfully applied to improve diagnostic accuracy in OSA detection. The performance of the proposed approach is superior to that of traditional audio analysis technology. The proposed method significantly reduces difficulties in OSA detection and diagnosis, such that potential OSA patients can perform initial inspections by themselves at home.

Genome-wide discovery of long intergenic noncoding RNAs and their epigenetic signatures in the rat.

Long intergenic noncoding RNAs (lincRNAs) play a crucial role in many biological processes. The rat is an important model organism in biomedical research. Recent studies have detected rat lincRNA genes from several samples. However, identification of rat lincRNAs using large-scale RNA-seq datasets remains unreported. Herein, using more than 100 billion RNA-seq reads from 59 publications together with RefSeq and UniGene annotated RNAs, we report 39,154 lincRNA transcripts encoded by 19,162 lincRNA genes in the rat. We reveal sequence and expression similarities in lincRNAs of rat, mouse and human. DNA methylation level of lincRNAs is higher than that of protein-coding genes across the transcription start sites (TSSs). And, three lincRNA genes overlap with differential methylation regions (DMRs) which associate with spontaneously hypertensive disease. In addition, there are similar binding trends for three transcription factors (HNF4A, CEBPA and FOXA1) between lincRNA genes and protein-coding genes, indicating that they harbour similar transcription regulatory mechanisms. To date, this is the most comprehensive assessment of lincRNAs in the rat genome. We provide valuable data that will advance lincRNA research using rat as a model.