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Background Coronary artery disease (CAD) is a global health concern. Conventional single photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a noninvasive method for assessing the severity of CAD. However, it relies on manual classification by clinicians, which can lead to visual fatigue and potential errors. Deep learning techniques have displayed promising results in CAD diagnosis and prediction, providing efficient and accurate analysis of medical images. Methods In this study, we explore the application of deep learning methods for assessing the severity of CAD and identifying cases of multivessel disease (MVD). We utilized the EfficientNet-V2 model in combination with DeepSMOTE to evaluate CAD severity using SPECT MPI images. Results Utilizing a dataset consisting of 254 patients (176 with MVD and 78 with single-vessel disease [SVD]), our model achieved an accuracy rate of 84.31% and area under the receiver operating characteristic curve (AUC) value of 0.8714 in predicting cases of MVD. These results underline the promising potential of our approach in MVD prediction, offering valuable diagnostic insights and the prospect of reducing medical costs. Conclusion This study emphasizes the feasibility of employing deep learning techniques for predicting MVD based on SPECT MPI images. The integration of Efficient-Net-V2 and DeepSMOTE methods effectively evaluates CAD severity and distinguishes MVD from SVD. Our research presents a practical approach to the early prediction and diagnosis of MVD, ultimately leading to enhanced patient outcomes and reduced healthcare costs.

Background: Post-acute sequelae of SARS-CoV-2 infection (PASC) affects patients after recovering from acute coronavirus disease 2019 (COVID-19). This study investigates the impact of SARS-CoV-2 vaccination on PASC symptoms in children in Taiwan during the Omicron pandemic. Methods: We enrolled children under 18 years with PASC symptoms persisting for more than 4 weeks. Data collected included demographics, clinical information, vaccination status, and symptom persistence. We used logistic regression models to compare symptoms in the acute and post-COVID-19 phases and to assess the association between vaccination and these symptoms. Results: Among 500 PASC children, 292 (58.4%) were vaccinated, 282 (52.8%) were male, and the mean (SD) age was 7.6 (4.6) years. Vaccinated individuals exhibited higher odds of experiencing symptoms in the previous acute phase, such as cough (adjusted odds ratio [AOR] = 1.57; 95% confidence interval [CI]: 1.02–2.42), rhinorrhea/nasal congestion (AOR = 1.74; 95% CI: 1.13–2.67), sneezing (AOR = 1.68; 95% CI: 1.02–2.76), sputum production (AOR = 1.91; 95% CI: 1.15–3.19), headache/dizziness (AOR = 1.73; 95% CI: 1.04–2.87), and muscle soreness (AOR = 2.33; 95% CI: 1.13–4.80). In contrast, there were lower odds of experiencing abdominal pain (AOR = 0.49; 95% CI: 0.25–0.94) and diarrhea (AOR = 0.37; 95% CI: 0.17–0.78) in children who had received vaccination during the post-COVID-19 phase. Conclusions: This study revealed clinical features and vaccination effects in PASC children in Taiwan. Vaccination may reduce some gastrointestinal symptoms in the post-COVID-19 phase.

Abstract We demonstrate that isomorphically mapping gray-level medical image matrices onto energy spaces underlying the framework of fast data density functional transform (fDDFT) can achieve the unsupervised recognition of lesion morphology. By introducing the architecture of geometric deep learning and metrics of graph neural networks, gridized density functionals of the fDDFT establish an unsupervised feature-aware mechanism with global convolutional kernels to extract the most likely lesion boundaries and produce lesion segmentation. An AutoEncoder-assisted module reduces the computational complexity from $$\mathcal{O}\left({N}^{3}\right)$$ O N 3 to $$\mathcal{O}\left(N\mathrm{log}N\right)$$ O N log N , thus efficiently speeding up global convolutional operations. We validate their performance utilizing various open-access datasets and discuss limitations. The inference time of each object in large three-dimensional datasets is 1.76 s on average. The proposed gridized density functionals have activation capability synergized with gradient ascent operations, hence can be modularized and embedded in pipelines of modern deep neural networks. Algorithms of geometric stability and similarity convergence also raise the accuracy of unsupervised recognition and segmentation of lesion images. Their performance achieves the standard requirement for conventional deep neural networks; the median dice score is higher than 0.75. The experiment shows that the synergy of fDDFT and a naïve neural network improves the training and inference time by 58% and 51%, respectively, and the dice score raises to 0.9415. This advantage facilitates fast computational modeling in interdisciplinary applications and clinical investigation.

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model’s reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.

BackgroundTraditional methods for investigating work hours rely on an employee’s physical presence at the worksite. However, accurately identifying break times at the worksite and distinguishing remote work outside the worksite poses challenges in work hour estimations. Machine learning has the potential to differentiate between human-smartphone interactions at work and off work. ObjectiveIn this study, we aimed to develop a novel approach called “probability in work mode,” which leverages human-smartphone interaction patterns and corresponding GPS location data to estimate work hours. MethodsTo capture human-smartphone interactions and GPS locations, we used the “Staff Hours” app, developed by our team, to passively and continuously record participants’ screen events, including timestamps of notifications, screen on or off occurrences, and app usage patterns. Extreme gradient boosted trees were used to transform these interaction patterns into a probability, while 1-dimensional convolutional neural networks generated successive probabilities based on previous sequence probabilities. The resulting probability in work mode allowed us to discern periods of office work, off-work, breaks at the worksite, and remote work. ResultsOur study included 121 participants, contributing to a total of 5503 person-days (person-days represent the cumulative number of days across all participants on which data were collected and analyzed). The developed machine learning model exhibited an average prediction performance, measured by the area under the receiver operating characteristic curve, of 0.915 (SD 0.064). Work hours estimated using the probability in work mode (higher than 0.5) were significantly longer (mean 11.2, SD 2.8 hours per day) than the GPS-defined counterparts (mean 10.2, SD 2.3 hours per day; P

An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients’ vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.

Background and objective: Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. Methods: In this retrospective study, CT images of 1070 T3–4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. Results: The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model’s capability to predict adverse clinical outcomes was superior to those of traditional assessments. Conclusion: AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques.

Abstract The extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of $$75.64\%$$ 75.64 % , while the result of segmentation achieves an IoU of $$84.83\%$$ 84.83 % and a DICE score of $$86.21\%$$ 86.21 % . Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

BackgroundIn recent years, the progress and generalization surrounding portable ultrasonic probes has made ultrasound (US) a useful tool for physicians when making a diagnosis. With the advent of machine learning and deep learning, the development of a computer-aided diagnostic system for screening renal US abnormalities can assist general practitioners in the early detection of pediatric kidney diseases. ObjectiveIn this paper, we sought to evaluate the diagnostic performance of deep learning techniques to classify kidney images as normal and abnormal. MethodsWe chose 330 normal and 1269 abnormal pediatric renal US images for establishing a model for artificial intelligence. The abnormal images involved stones, cysts, hyperechogenicity, space-occupying lesions, and hydronephrosis. We performed preprocessing of the original images for subsequent deep learning. We redefined the final connecting layers for classification of the extracted features as abnormal or normal from the ResNet-50 pretrained model. The performances of the model were tested by a validation data set using area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. ResultsThe deep learning model, 94 MB parameters in size, based on ResNet-50, was built for classifying normal and abnormal images. The accuracy, (%)/area under curve, of the validated images of stone, cyst, hyperechogenicity, space-occupying lesions, and hydronephrosis were 93.2/0.973, 91.6/0.940, 89.9/0.940, 91.3/0.934, and 94.1/0.996, respectively. The accuracy of normal image classification in the validation data set was 90.1%. Overall accuracy of (%)/area under curve was 92.9/0.959.. ConclusionsWe established a useful, computer-aided model for automatic classification of pediatric renal US images in terms of normal and abnormal categories.

Abstract Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.

Left ventricular diastolic dysfunction (LVDD) occurs at the initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features have been applied to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. Three machine learning classifiers including random forest (RF), support vector machine (SVM) and gradient boosting decision tree (GBDT) for the input of 26 ECG features with or without 6 other biological features (age, anthropometrics and blood pressures) are compared with the corrected QT (QTc) interval, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the following echocardiographic criteria: (1) an E/A ratio of mitral inflow < 0.8; (2) a lateral mitral annulus velocity e’ < 10 cm/s; and (3) an E/e’ ratio >14. The best areas under the receiver operating characteristic curve were observed in machine learning of the RF for ECG only (84.1%) and of the SVM for all ECG and biological features (82.1%), both of which were superior to the QTc interval (64.6%). If the specificity is chosen to be approximately 75.0%, the sensitivity of the RF for ECG only reaches 81.0% and that of the SVM for all ECG and biological features is raised to 85.7%, both of which are higher than 47.6% by the QTc interval. This study suggests that using machine learning for ECG features only or with other biological features to predict LVDD in young Asian adults is reliable. The proposed methods provide for the early detection of LVDD for young adults and are helpful for taking preventive action on heart failure.

Objective: The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH. Method: In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm. Results: On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH. Conclusion: We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.

Online multiclass classification is a specific problem of online learning that performs a sequence of multiclass classification tasks given the knowledge of previous tasks. The goal is to make correct predictions for this sequence. It is generally considered a more complicated problem than its binary counterpart, online binary classification. A popular algorithm, called the passive-aggressive algorithm, was primarily proposed for binary problems and later extended as the multiclass passive-aggressive (MPA) algorithm for multiclass problems. The nature of MPA allows itself to implement the kernel trick, which enables us to make better predictions with a kernel-based model. However, this approach suffers from the curse of kernelization that causes unbounded growth of the model in memory usage and runtime. To solve the growth problem, we first introduce a resource perspective that gives an alternative and equivalent interpretation of the kernel-based MPA algorithm. Based on the resource perspective, we propose the budgeted MPA (BMPA) algorithm, which approximates the kernel-based MPA algorithm. BMPA limits the maximum number of available resources by removal and fully exploits them through a constrained optimization. We study three removal strategies and give a relative mistake bound that provides a unified analysis. Simulation experiments on various datasets are conducted to demonstrate that BMPA is effective and competitive with state-of-the-art budgeted online algorithms.

Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization.

Tobacco smoking has been found associated with lower cardiorespiratory fitness in white and black males; however, few studies have not been conducted to clarify such relationship in Asian males. We performed a cross-sectional study to investigate the association between tobacco smoking status and physical fitness in 3,669 military males, averaged 29.4 years of age, from the cardiorespiratory fitness and hospitalization events in armed forces (CHIEF) study in Taiwan during 2014. There were 1,376 current smokers, and the others were noncurrent smokers. The effective sample size estimated was 1,230 participants, as the margin of error was ±3% at the 99% confidence level. Physical fitness was evaluated by time for a 3000-meter run test (aerobic fitness) and repetitive numbers of 2-minute sit-ups and 2-minute push-ups (anaerobic fitness) where all procedures were standardized by using computerized scoring systems. A multiple linear analysis adjusting for age, service specialty, body mass index, heart rate, alcohol intake, and training frequency was used to determine the relationship. As compared with noncurrent smoking, current smoking was inversely correlated with longer time for a 3000-meter run (β = 15.66 (95% confidence intervals (CI): 10.62, 20.70)) and fewer repetitive numbers of 2-minute sit-ups and 2-minute push-ups (β = −1.53 (95% CI: −2.08, −0.97) and −1.31 (95% CI: −2.12, −0.50), respectively). Our finding reconfirms the concept that tobacco smoking might reduce both aerobic and anaerobic fitness among young Asian males.

Background: The global spread of carbapenem-resistant Acinetobacter baumannii (CRAB) is now a public health problem. In Taiwan, the relationship of the CRAB circulation between long-term care facilities (LTCFs) and acute care hospitals remains unclear. Here, we use molecular epidemiologic methods to describe the transmission of CRAB isolates between a community hospital and its affiliated LTCFs. Methods: Subjects localized in eight LTCFs who were not admitted acute care hospitals in recent a year were enrolled in this study. CRAB isolates were collected during June 1, 2015 and December 31, 2015. DNA fingerprinting was performed by repetitive extragenic palindromic sequence-based polymerase chain reaction (Rep-PCR) and multilocus sequence typing (MLST). Multiplex-PCR amplification for the detection of blaOXA genes and beta-lactamase genes was performed. Results: Twenty one subjects were enrolled. The major hospital admission diagnoses among the 21 subjects were pneumonia (71.4%). Genotyping of CRAB isolates by Rep-PCR revealed that a major clone, designated as type III, comprised fifteen of 21 (71.4%) isolates taken from 5 LTCFs and one study hospital. The isolates with type III were subtyped by PubMLST into 4 ST types. The most prevalent blaOXA genes in these isolates were blaOXA-23-like (85.70%, 18/21). Twenty isolates carried blaSHV. Conclusion: Clonal spread of blaOxA-23-carrying CRABs was found around LTCFs and the affiliated hospital. In Taiwan, it is important for the government to focus attention on the importance of identifying and tracing CRAB infections in LTCFs.

Abstract Background A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. Results Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. Conclusions Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a.

[This corrects the article DOI: 10.1371/journal.pone.0185475.].

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

This study analyzes CZT SPECT myocardial perfusion images that are collected at Chang Gung Memorial Hospital, Kaohsiung Medical Center in Kaohsiung. This study focuses on the classification of myocardial perfusion images for coronary heart diseases by convolutional neural network techniques. In these gray scale images, heart blood flow distribution contains the most important features. Therefore, data-driven preprocessing is developed to extract the area of interest. After removing the surrounding noise, the three-dimensional convolutional neural network model is utilized to classify whether the patient has coronary heart diseases or not. The prediction accuracy, sensitivity, and specificity are 87.64%, 81.58%, and 92.16%. The prototype system will greatly reduce the time required for physician image interpretation and write reports. It can assist clinical experts in diagnosing coronary heart diseases accurately in practice.

The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN). For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN) model based on techniques of hidden Markov model (HMM), likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.

Techniques of automatic medical image segmentation are the most important methods for clinical investigation, anatomic research, and modern medicine. Various image structures constructed from imaging apparatus achieve a diversity of medical applications. However, the diversified structures are also a burden of contemporary techniques. Performing an image segmentation with a tremendously small size (1024 pixels by 1024 pixels) becomes a challenge in perspectives of both technical feasibility and theoretical development. Noise and pixel pollution caused by the imaging apparatus even aggravate the difficulty of image segmentation. To simultaneously overcome the mentioned predicaments, we propose a new method of medical image segmentation with adjustable computational complexity by introducing data density functionals. Under this theoretical framework, several kernels can be assigned to conquer specific predicaments. A square-root potential kernel is used to smoothen the featured components of employed images, while a Yukawa potential kernel is applied to enhance local featured properties. Besides, the characteristic of global density functional estimation also allows image compression without losing the main image feature structures. Experiments on image segmentation showed successful results with various compression ratios. The computational complexity was significantly improved, and the score of accuracy estimated by the Jaccard index had a great outcome. Moreover, noise and regions of light pollution were mostly filtered out in the procedure of image compression.

One great challenge of genomic research is to efficiently and accurately identify complex gene regulatory networks. The development of high-throughput technologies provides numerous experimental data such as DNA sequences, protein sequence, and RNA expression profiles makes it possible to study interactions and regulations among genes or other substance in an organism. However, it is crucial to make inference of genetic regulatory networks from gene expression profiles and protein interaction data for systems biology. This study will develop a new approach to reconstruct time delay boolean networks as a tool for exploring biological pathways. In the inference strategy, we will compare all pairs of input genes in those basic relationships by their corresponding p-scores for every output gene. Then, we will combine those consistent relationships to reveal the most probable relationship and reconstruct the genetic network. Specifically, we will prove that O(log n) state transition pairs are sufficient and necessary to reconstruct the time delay boolean network of n nodes with high accuracy if the number of input genes to each gene is bounded. We also have implemented this method on simulated and empirical yeast gene expression data sets. The test results show that this proposed method is extensible for realistic networks.

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network.

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes.

Background: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person dies of heart disease every 26 min worldwide. Deep learning approaches are characterized by the appropriate combination of abnormal features based on numerous annotated images. The constructed convolutional neural network (CNN) model can identify normal states of reversible and irreversible myocardial defects and alert physicians for further diagnosis. Methods: Cadmium zinc telluride single-photon emission computed tomography myocardial perfusion resting-state images were collected at Chang Gung Memorial Hospital, Kaohsiung Medical Center, Kaohsiung, Taiwan, and were analyzed with a deep learning convolutional neural network to classify myocardial perfusion images for coronary heart diseases. Results: In these grey-scale images, the heart blood flow distribution was the most crucial feature. The deep learning technique of You Only Look Once was used to determine the myocardial defect area and crop the images. After surrounding noise had been eliminated, a three-dimensional CNN model was used to identify patients with coronary heart diseases. The prediction area under the curve, accuracy, sensitivity, and specificity was 90.97, 87.08, 86.49, and 87.41%, respectively. Conclusion: Our prototype system can considerably reduce the time required for image interpretation and improve the quality of medical care. It can assist clinical experts by offering accurate coronary heart disease diagnosis in practice. [ABSTRACT FROM AUTHOR]