Meibomian Gland Dysfunction (MGD) is a key contributor to clinical Dry Eye Disease (DED), and accurate meibomian gland segmentation is essential for its intelligent diagnosis. However, the high cost of annotation remains a major barrier to improving segmentation quality. Active Learning (AL), which selects the most informative samples for annotation, offers an efficient solution. This study explores the application of AL to infrared meibography image segmentation and introduces a novel progressive hybrid sampling framework. Initial AL stages often rely on random sampling for initial labeled sets, risking unstable performance from uninformative data and cold starts. Additionally, meibography images always have specular reflections whose late introduction in training degrades future segmentation accuracy. To address these challenges, we incorporate prior knowledge of specular reflections into the initial labeled set construction, enabling the model to handle such artifacts from the beginning. We then implement a two-stage dynamic sampling strategy: entropy-based uncertainty sampling is used in the early iterations to maximize annotated data informativeness and rapidly boost model performance. However, as AL progresses, only focusing on uncertainty may overlook global data distribution and lead to redundant annotations. Hence, an adaptive threshold is introduced to monitor sample redundancy. When redundancy exceeds this threshold, a diversity sampling module is activated to improve model generalization. Experimental results on the public MGD-1K dataset show that our method achieves superior segmentation performance under the same annotation budget. Unlike mainstream methods that often fail to outperform random baselines, our approach consistently delivers accurate and efficient meibomian gland segmentation.

This study examined whether adipose tissue from different anatomical regions differentially influences autophagya cellular recycling process-in human pancreatic$\boldsymbol{\beta}$-cells, and whether the IL-8-CXCR2 signaling pathway mediates this effect. To investigate, human pancreatic$\boldsymbol{\beta}$-cells with either suppressed or overexpressed CXCR2 were co-cultured with various adipose tissues using Transwell chambers. Expression levels of CXCR2 and the autophagy-related proteins SQSTM1 and LC3B were measured. We found that adipose tissue consistently upregulated SQSTM1 protein expression in$\boldsymbol{\beta}$-cells, with no significant effect on LC3B. CXCR2 modulation was pivotal: suppression of CXCR2 reduced SQSTM1 levels and significantly increased LC3B expression, suggesting enhanced autophagic activity. Conversely, CXCR2 overexpression in$\beta$-cells co-cultured with visceral adipose tissue (VAT) significantly elevated SQSTM1 levels, while LC3B expression remained unchanged. These findings suggest the IL-8CXCR2 pathway selectively influences components of the autophagic process. Notably, subcutaneous adipose tissue (SAT) was more effective than VAT in increasing SQSTM1 expression in$\beta$-cells. This depot-specific variability, combined with CXCR2dependent effects, highlights a nuanced role for IL-8-CXCR2 signaling in mediating adipose tissue-induced changes in$\beta$-cell autophagy. In conclusion, distinct adipose tissue depots differentially regulate autophagy-related protein expression in human pancreatic$\beta$-cells, with the IL-8-CXCR2 pathway acting as a key modulator. These insights deepen our understanding of the complex crosstalk between adipose tissue and pancreatic$\beta$-cell function, particularly in relation to cellular self-digestion and stress adaptation processes, and may inform future therapeutic strategies targeting metabolic diseases such as diabetes.

In real-world clinical settings, fundus images often suffer from quality degradation caused by uncontrolled imaging conditions, with underexposure being especially common. Such low-light images degrade visual quality and obscure critical retinal structures, hindering diagnosis and automated analysis. To address this issue, we propose a structure-aware unsupervised GAN-based enhancement method for low-light fundus images. The generator employs an attention mechanism that fuses estimated illumination with high-frequency components to enhance brightness while preserving fine anatomical details. A dualdiscriminator framework ensures both global consistency and local detail fidelity: the multi-scale global discriminator enforces structural coherence, and the local discriminator refines regional illumination to prevent over- or underexposure. Quantitative and qualitative results on the EyeQ dataset, along with downstream tasks such as vessel segmentation and diabetic retinopathy (DR) grading, demonstrate that our method significantly improves brightness and contrast while maintaining retinal structure, supporting reliable clinical and computational analysis.

Lipogenesis is a metabolic process regulated by insulin and glucose, which governed by specific transcription factors. This study aims to explore the expression of adipocyte-specific genes in human subcutaneous (SAT) and visceral (VAT) adipose tissues among diverse populations. Anthropometric characteristics and relevant biochemical indicators were assessed in a cohort of 50 subjects from the Han population residing in Yunnan, China. The expression levels of key transcription factors (VDR, C/EBPα, PPARγ, and SREBP-1c) were quantified using real-time polymerase chain reaction (qRT-PCR) in SAT and VAT samples obtained from 20 obese/overweight patients and 30 individuals of normal weight. The results showed that among normal-weight patients, VDR mRNA levels were significantly higher in SAT compared to VAT. Conversely, in obese/overweight patients, PPARγ and SREBP-1c mRNA expressions were elevated in VAT relative to SAT. Notably, C/EBPα exhibited high expression levels in both SAT and VAT, irrespective of weight status. Spearman’s rank correlation coefficients revealed positive associations between VDR and PPARγ, as well as between PPARγ and SREBP-1c in SAT. Conversely, in VAT, VDR correlated positively with SREBP-1c, which in turn exhibited a positive correlation with PPARγ. Additionally, PPARγ demonstrated a positive correlation with C/EBPα in both SAT and VAT. In conclusion, obese/overweight individuals exhibited lower gene expression levels of VDR, PPARγ, and SREBP-1c in SAT compared to VAT. While high expression of C/EBPα may have minimal implications for obesity, it is essential for maintaining the terminal differentiation phenotype of adipocytes. Furthermore, the interaction patterns among these transcription factors varied between fat depots.

To enhance the diagnostic efficiency and accuracy of gastric mucosal lesions in magnetically controlled capsule endoscopy (MCCE) images, a Jigsaw Guided Deep Feature Fusion (JG-DFF) artificial intelligence (AI)-assisted recognition model based on the ResNet-50 convolutional neural network (CNN) is presented in this paper. Based on a dataset consisting of 4,053 MCCE images retrospectively collected from Sun Yat-sen Memorial Hospital, we constructed a ResNet-50 AI recognition model, as well as a JG-DFF AI recognition model under the jigsaw guided deep feature fusion. Additionally, the diagnostic ability and run time of the JG-DFF model were compared against a digestive endoscopist using the same data. The results indicate that the JG-DFF model has the potential for clinical application in improving diagnostic efficiency and reducing false positive rates. The overall accuracy of the JG-DFF artificial intelligence assisted recognition model established in this study is 92.1%. Compared to the ResNet-50 model, recognition by the JG-DFF model was more consistent; Compared with the digestive endoscopist, observed specificity of the JG-DFF model in identifying positive lesions was similar (P>0.05). That said, the JG-DFF model was more sensitive in identifying certain classes (P1

Currently, there is growing interest in understanding relationships among vitamin D, bone metabolism indicators, and diabetes. Numerous studies have highlighted vitamin D deficiency as a risk factor not only for obesity but also for fluctuations in blood glucose levels and insulin resistance. Human adipose tissue is categorized into visceral fat and subcutaneous fat based on its distribution. Although visceral fat comprises a smaller proportion compared to subcutaneous fat, it is considered to have a much more significant impact on various diseases such as obesity and diabetes. However, precise relationships among different adipose tissues, vitamin D deficiency, and bone metabolism-related indicators remain unclear. Furthermore, it is uncertain whether the effects of vitamin D on bone metabolism differ between individuals with normal visceral fat and those with excessive visceral fat. Moreover, to date, most research examining the relationship between vitamin D and fat distribution has focused specifically on the vitamin D precursor produced by the liver, 25(OH)D. Importantly, determining 25(OH)D levels constitutes an indirect measure of vitamin D enumerating an inactive intermediate in the pathway generating the active form of vitamin D, 1,25(OH)2D3. As such, this study includes 1,25(OH)2D3 in the research indicators, providing a more comprehensive and direct understanding of the relationships among vitamin D, adipose tissue distribution, and bone metabolism. In addition, this study aims to investigate not only the impact of normal visceral fat and excessive visceral fat on bone metabolism indicators but also to compare the differences between subcutaneous fat and visceral fat contributing to bone metabolism.

To solve the label scarcity of meibomian gland segmentation in infrared meibography images, a novel framework for semi-supervised meibomian gland segmentation is firstly presented in this paper. Extra mutual feature consistency constraint is added along with the cross pseudo supervision , guiding the model more robustness and discriminative. Meanwhile, cross uncertainty rectification is introduced to avoid noisy labels, further improving the pseudo supervision. Experimental results on an internal dataset reveals that our method yields significant performances using only 10% of the labeled data compared to the fully supervised segmentation, and outperforms the state-of-the art semi-supervised segmentation methods. Combination of mutual consistency regularization and cross uncertainty rectifi-cation guides model to distinguish glands from background well with limited labeled data.

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.

It is well known that microRNAs (miRNAs or miRs) are small (~18-25 nt) yet highly potent non-coding RNA-derived RNAs (ndRNAs), originating from pre-miRNA fragmentation, that have been shown to alter the post-transcriptional functionality of many messenger RNAs (mRNAs). Biologically, the identification and study of miRNAs is very critical due to their increasing significance as biomarkers for many types of cancers and other genetic diseases. While empirical evidence supporting the existence of several novel ndRNAs excised from other longer non coding RNAs (ncRNAs) is growing, recent evidence suggests the full extent of their prevalence is likely underappreciated. Although some computational methods have been designed to help domain experts identify and understand miRNAs by analyzing Next Generation Sequencing (NGS) datasets, there are some crucial challenges, such as efficiency, effectiveness, and generalizability, in the state-of-the-art in-silico methods. To address such problems, our group proposed a new algorithm to mine ndRNAs by applying wavelet-based signal processing techniques as opposed to the current string-based NGS sequence alignment/analysis. However, due to novelty of the approach, our initial version of the algorithm was focused specifically on mining miRNAs, snoRNA-derived RNAs (sdRNAs) and transfer RNA (tRNA) fragments (tRFs) because of their importance in the literature plus the availability of experimentally validated databases to confirm our findings. Despite the computational issues, we still lack a basic understanding of the existence and the range of ndRNA functionalities from a) ndRNAs other than miRs, sdRNAs & tRFs in humans, and b) all ndRNAs in millions of organisms other than humans. Hence, there is an urgent requirement to automate the extraction and experimentation of ndRNAs, especially considering the rate at which NGS data is being produced. Therefore, in the current article, we extended our algorithm to be applicable to ~500 organisms—including eukaryotes, plants, bacteria, fungi, and protists—along with all their ncRNAs available in the current NCBI annotation. We also constructed a real-time user-friendly platform, SURFR, available at salts.soc.southalabama.edu/surfr, to aid domain experts and the aspiring biomedical scientists to perform RNA-Seq experiments to study ndRNAs. Not only our platform is extremely efficient, but we are also capable of allowing the users to identify, analyze, visualize, and compare ndRNAs from up to 30 NGS files to perform rigorous experimentation. Moreover, access to NGS files from public databases like SRA, and ndRNAs from private databases like TCGA are made readily available to the users to further validate their novel findings. Finally, we provide theoretical validation to examine our platform’s effectiveness.

The Common Data Elements (CDEs) standard of the International organization for Standardization (ISO) 11179 is commonly used in the field of clinical data processing. The Biomedical Research Integrated Domain Group (BRIDG) model is the framework for biomedical and clinical research. Mapping CDEs to BRIDG (also known as CDE classification) would help with interoperability and data analysis in the field of clinical research. That said, manually mapping CDEs to their corresponding BRIDG class is highly time-consuming and labor-intensive. In this paper we present a new classification algorithm along with a new oversampling method. Our algorithm uses the Term Frequency-Inverse Document Frequency (TF-IDF) as the feature representation method. By assigning different weights to various attributes, we enable more important attributes to perform more important roles during the mapping process. In addition, the oversampling method generates every new attribute in the minor class by picking the length and setting the word of the new attribute according to the existing training set. Our research outcomes demonstrate significant contributions to the field in the following ways: (1) Generation of a new CDE classification algorithm that outperforms existing algorithms in the literature, including the Random Forest Classifier, Linear Support Vector Classification (SVC), Multinomial Naive Bayes (NB), Logistic Regression, and Long Short-Term Memory (LSTM) networks, in terms of accuracy, precision, recall, and F-1 score measures. (2) Generation of a new oversampling method able to improve CDE classification accuracy for Random Forest and Multinomial NB. (3) Our classification algorithm employs two novel attributes, namely “Data Element Preferred Definition” and “Document,” which are more efficient at classifying CDEs than the six attributes traditionally selected by domain experts.

Polysomnography (PSG) screening for obstructive sleep apnea (OSA) is time consuming. The OSA classification is very important for medical scientists and machine learning researchers. In the current work, we developed a classification method for electrocardiogram (ECG) data. The data set has two labels: sleep disorders or not. As a result, Active Learning is used as a classification technique. Data stream classification in a non-stationary environment is attaining more attention recently. It is a highly challenging task, since the concept drift and limited labeled data. Therefore, a classification model is needed to be struggling with concept drift detection and the need of labeled data. To solve these issues, we propose an efficient semi-supervised method in this paper which uses Active Learning to detect concept drift in an unsupervised way and Classifiers Ensemble to keep higher predictions combined with weighted majority voting. Experiments results on real-world and synthetic data-sets show the effectiveness of the proposed approach. For the initial experiment, we use an existing data set. The data set includes data for every 10 seconds, up to 6000 seconds, and 35 patients. We have used 80% of the data for training purposes and 20% of the data for testing purposes. Active Learning results show that our method can effectively detect OSA. The accuracy of the predicted result is 71%. Future research in this area will be to obtain data from hospitals and use our developed algorithms for OSA classification and prediction.

Hormones such as fibroblast growth factor 21 (FGF21) and glucocorticoids (GCs) play crucial roles in bone metabolism. Long-term treatment with corticosteroid drugs may lead to glucocorticoid-related osteoporosis, and previous studies have defined FGF21 as a factor may participate in bone metabolism and most studies showed that it negatively regulates bone metabolism. Herein we examine the interplay between these factors in bone metabolism. In our study, expressions of PPAR, ALP, and TRAP-5b were compared between WT mice and FGF21 inhibited mice (FGFi mice) and WT and FGFi mice treated with adrenocorticotropic hormone (ACTH). We found ACTH treatment significantly blocked ALP decreases in FGFi mice as compared to WT whereas TRAP-5b expression was significantly increased (p<; 0.01). Collectively, our findings suggest the following: FGF21 may serve as a negative regulator of bone metabolism, the negative regulation of bone metabolism via FGF21 may require the PPARγ pathway, and FGF21 may be involved in the ACTH-Cortisol axis-related bone mass loss.

Noncoding RNAs (ncRNAs) regulate gene expression in essential cellular processes and play key roles in many human diseases. Small nucleolar RNAs (snoRNAs) are a relatively large group of ncRNAs. Many studies have identified significant alterations of different snoRNAs in prostate, breast, and lung malignancies and that many of these snoRNAs have been shown to be processed into microRNA-like molecules known as snoRNA-derived RNAs (sdRNAs). Similarly, several small RNAs have also recently been found to be excised from well characterized tRNAs to function in both normal cellular metabolism and disease. In this paper, we present a new computational methodology for the identification, analysis, and visualization of ncRNA-derived RNAs (ndRNAs) with linear time and space complexity (named SURFr for Short Uncharacterized R NA Finder). We provide the results of our algorithm's running time by analyzing several publicly available datasets and finally discuss future research directions to our work.

The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.

Obstructive sleep apnea syndrome (OSAS) is a breathing disorder presenting during sleep. Although polysomnography (PSG) is the gold standard to diagnose OSAS, it is an expensive method that is quite complicated to use. Worse, it takes a long time between testing and getting a diagnosis from PSG. Thus, we have designed an algorithm aimed at diagnosing OSAS in a more efficient manner. First, blood oxygen saturation (SpO2) data are processed to obtain statistical features, which are then trained to establish a classification model based on a support vector machine (SVM) strategy; the resulting SVM model performs the diagnosis of OSAS. Furthermore, in order to allow remote diagnosis, we combine our algorithm with a monitoring system. To achieve this, physiological data are collected from a smart phone and then uploaded to the SVM model in the cloud. Once processed, a diagnosis report is returned to the smart phone. A preliminary evaluation of our algorithm based on real-world data is extremely promising as we find its accuracy, sensitivity, and specificity to be 90.2%, 87.6%, and 94.1%, respectively.

The incidence of hypertension has recently shown a significant increase in young people, with aerobic exercise intervention being recognized as an effective approach to decrease blood pressure (BP). However, BP response to aerobic exercise can be highly individualized, and no research has been conducted on predicting the effect of aerobic exercise intervention for reducing BP in young hypertensive patients. In this work, we use the data generated from a cardiopulmonary exercise test (CPET) in young hypertensive patients (before aerobic exercise intervention) to derive information from multiple cardiopulmonary metabolic indices. The data, presented as time series, are then analyzed by a machine learning method to predict the effect of aerobic exercise intervention in lowering BP. This study provides several novel insights for making personalized aerobic exercise intervention programs for young adults with stage I hypertension.

The six papers in this special section were presented at the IEEE BIBM 2015 conference that was held in Washington, D.C., November 9-12, 2015. The scientific program highlighted five themes to provide breadth, depth, and synergy for research collaboration: (1) genomics and molecular structure, function, and evolution; (2) computational systems biology; (3) medical informatics and translational bioinformatics; (4) cross-cutting computational methods and bioinformatics infrastructures; and (5) healthcare informatics,

Acute lymphoblastic leukemia (ALL) is the most prevalent neoplasia among children, and drug resistance is still a major cause of chemotherapy failure in pediatric ALL. Prior research has shown that microRNAs (miRNAs) can play roles in such phenomenon, but detailed regulation mechanisms are far from completely understood. We utilized a semantic analysis approach and its MeSH term filtering mechanisms to explore the important roles of miRNA::mRNA regulation on glucocorticoid (GC) resistance in pediatric ALL. Hsa-miR-142-3p and hsa-miR-17-5p were predicted in this study to closely relate to GC resistance, thus potentially serving as novel biomarkers in pediatric ALL.

Glioblastoma multiforme (GBM) is the most malignant brain tumor with rapid relapse, and an early biomarker identification in GBM is of high importance. We introduced our computational methodologies to identify serum microRNAs (miRNAs) as novel biomarkers in GBM. Differentially expressed miRNAs (DEMs) in GBM were analyzed from the Gene Expression Omnibus (GEO) repository; we then combined Venn diagrams and OmniSearch, a semantic search tool, to filter out miRNAs that can regulate GBM disease process. Our results indicated that miR-142-3p, let-7c, and miR-140-5p are candidate biomarkers in GBM.

High-sensitivity C-reactive protein (hs-CRP) performs important roles on the onset of metabolic syndrome and cardiovascular diseases (CVD), but little is known about association between hs-CRP and obesity-related metabolic abnormalities in young people without classical CVD risk factors. It thus motivated us to investigate association among hs-CRP, body fat mass (FM) distribution, and other cardiometabolic risk factors in young healthy women. Our approach is based on biological experiments, statistical analysis, and semantic search. Research outcomes in this study resulted in two novel discoveries. Discovery 1: There are four primary determinants for hs-CRP, i.e., central/abdominal FM (a.k.a. trunk FM) accumulation, leptin, high density lipoprotein cholesterol (HDL-C), and plasminogen activator inhibitior-1 (PAI-1). Discovery 2: Chronic inflammation may involve in adipocyte-cytokine interaction underlying the metabolic derangement in healthy young women.

Chronic obstructive pulmonary disease (COPD) and lung cancer (LC) are two serious diseases that present a major health problem worldwide. However, genetic contribution to both diseases remains unclear, including various regulation mechanisms at genetic level resulting in the progression from COPD to LC. In this paper, we describe our comprehensive methodologies, which seamlessly integrate both biological (conducted in “wet labs”) and computational (based on domain ontologies and semantic technologies) approaches, to investigate the important role of microRNA::mRNA regulations performed in COPD and LC. We discovered two genes, RGS6 and PARK2, that are strongly associated with the risk of developing either COPD or LC or both; additionally, we also identified two sets of microRNAs that are computationally predicted to regulate RGS6 and PARK2, respectively. These microRNAs can be further biologically verified in the future and serve as novel biomarkers in COPD and/or LC.

Osteoarthritis (OA) is the most common joint disease worldwide, resulting in severe joint pain and significantly decreased life quality in the elderly population. Due to the lack of effective medicine that can reverse the degeneration of articular cartilage, ultimately all OA patients need to receive an artificial joint replacement surgery. Although the pathogenesis and progression of OA has attracted a lot research activities, most regulation mechanisms at genetic and genomics level have not yet been completely understood. In this paper, we introduce the utilization of OmniSearch, a semantic search software tool, to facilitate the exploration of important roles performed by numerous microRNA molecules on OA disease process. Our promising experimental results have indicated that the methodologies described in this paper are able to help effectively unraveling critical microRNA regulation mechanisms. Consequently, we can facilitate clinical investigators' efforts in the understanding of OA pathogenesis, and thus contributing to early diagnosis and effective treatment in OA in the future.

Diabetic kidney disease (DKD) is a serious disease that presents a major health problem worldwide. There is a desperate need to explore novel biomarkers to further facilitate the early diagnosis and effective treatment in DKD patients so that to prevent them to develop end-stage renal disease (ESRD). However, most of regulation mechanisms at genetic level in DKD still remain unclear. In this work-in-progress paper, we describe our innovative methodologies that integrate biological, statistics, and computational approaches to investigate important roles performed by regulations among microRNAs (miRs), long non-coding RNAs (lncRNAs), and messenger RNAs (mRNAs) in DKD. We conducted a series of experiments and identified a list of miRs and lncRNAs as potential novel biomarkers, along with the set of target genes regulated by discovered miRs. Our initial analysis results are promising in better understanding regulation mechanisms of miRs and lncRNAs on the pathogenesis and progression of DKD.

Identification of non-coding RNAs (ncRNAs) has been significantly enhanced due to the rapid advancement in sequencing technologies. On the other hand, semantic annotation of ncRNA data lag behind their identification, and there is a great need to effectively integrate discovery from relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a precisely defined ncRNA controlled vocabulary, which can fill a specific and highly needed niche in unification of ncRNA biology.

Research has indicated that microRNAs (miRNAs), a special class of non-coding RNAs (ncRNAs), can perform important roles in different biological and pathological processes. miRNAs' functions are realized by regulating their respective target genes (targets). It is thus critical to identify and analyze miRNA-target interactions for a better understanding and delineation of miRNAs' functions. However, conventional knowledge discovery and acquisition methods have many limitations. Fortunately, semantic technologies that are based on domain ontologies can render great assistance in this regard. In our previous investigations, we developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), to provide the community with common data elements and data exchange standards in the miRNA research. This paper describes (1) our continuing efforts in the OMIT ontology development and (2) the application of the OMIT to enable a semantic approach for knowledge capture of miRNA-target interactions.

microRNAs (miRNAs) are special non-coding RNAs that perform important roles through their target genes. Biologists' conventional miRNA knowledge discovery is time-consuming, labor-intensive, and error-prone. Semantic technologies, which are created upon domain ontologies, can greatly enhance miRNA knowledge discovery. Unfortunately, yet no specific miRNA domain ontologies currently exist. It thus motivates the construction of a miRNA ontology. In addition, a manual ontology development has many drawbacks. We present in this paper a semi-automated ontology development methodology. The developed ontology is the very first one of its kind that formally encodes miRNA domain knowledge. It aims to provide data exchange standards and common data elements and thus help identify novel data connections among heterogeneous sources.

Continued surveillance of post-marketing Adverse Drug Events (ADEs) is considered essential for patient safety, and Electronic Health Records (EHRs) serve as a critical source for identifying relevant information. But effective EHR knowledge discovery and data mining is not trivial because involved data usually have significantly different semantics among each other. Semantic technologies are believed to greatly assist in this regard; unfortunately, semantic technologies and conventional data mining remain largely separate disciplines, and the fusion of these two disciplines is still in its infancy. This position paper explores two semantics-driven frequent data pattern mining algorithms for EHR knowledge discovery, aiming at more effective ADE monitoring in a population. By effectively utilizing human knowledge formally encoded in EHR domain ontologies, our proposed algorithms will enhance the identification of the drug ADE causality out of large amounts of heterogeneous data sets. Through mining a large corpus of representative EHRs at semantic level, we will be able to compile a comprehensive list of ADE endpoints by obtaining critical, but originally hidden and implicit, frequent data patterns. Ultimately, our software to be developed will significantly facilitate effective ADE monitoring and prediction. Moreover, our research is expected to produce broader impacts on the pharmaceutical industry by reducing the R & D cost for new drug discovery and on transforming current pharmacovigilance methods to reduce adverse events and hence improve human health.