Durvalumab with or without tremelimumab plus chemotherapy in HRR non-mutated, platinum-resistant ovarian cancer (KGOG 3045): A phase II umbrella trial.

AIM: We investigated the efficacy and safety of durvalumab (D) with or without tremelimumab (T) in addition to single-agent chemotherapy (CT) in patients with platinum-resistant recurrent ovarian cancer (PROC) lacking homologous recombination repair (HRR) gene mutations. PATIENTS AND METHODS: KGOG 3045 was an open-label, investigator-initiated phase II umbrella trial. Patients with PROC without HRR gene mutations who had received ≥2 prior lines of therapy were enrolled. Patients with high PD-L1 expression (TPS ≥25%) were assigned to arm A (D + CT), whereas those with low PD-L1 expression were assigned to arm B (D + T75 + CT). After completing arm B recruitment, patients were sequentially assigned to arms C (D + T300 + CT) and D (D + CT). RESULTS: Overall, 58 patients were enrolled (5, 18, 17, and 18 patients in arms A, B, C, and D, respectively). The objective response rates were 20.0, 33.3, 29.4, and 22.2%, respectively. Grade 3-4 treatment-related adverse events were observed in 20.0, 66.7, 47.1, and 66.7 of patients, respectively, but were effectively managed. Multivariable analysis demonstrated that adding T to D + CT improved progression-free survival (adjusted HR, 0.435; 95% CI, 0.229-0.824; P = 0.011). Favorable response to chemoimmunotherapy was associated with MUC16 mutation (P = 0.0214), high EPCAM expression (P = 0.020), high matrix remodeling gene signature score (P = 0.017), and low FOXP3 expression (P = 0.047). Patients showing favorable responses to D + T + CT exhibited significantly higher EPCAM expression levels (P = 0.008) and matrix remodeling gene signature scores (P = 0.031) than those receiving D + CT. CONCLUSIONS: Dual immunotherapy with chemotherapy showed acceptable response rates and tolerable safety in HRR non-mutated PROC, warranting continued clinical investigation.

Development and validation of a prediction model for postoperative urinary retention after prolapse surgery: A retrospective cohort study.

BACKGROUND: Postoperative urinary retention (POUR), a common condition after prolapse surgery with potential serious sequelae if left untreated, lacks a clearly established optimal timing for catheter removal. This study aimed to develop and validate a predictive model for postoperative urinary retention lasting > 2 and > 4 days after prolapse surgery. METHODS: We conducted a retrospective review of 1,122 patients undergoing prolapse surgery. The dataset was divided into training and testing cohorts. POUR was defined as the need for continuous intermittent catheterization resulting from a failed spontaneous voiding trial, with passing defined as two consecutive voids ≥ 150 mL and a postvoid residual urine volume ≤ 150 mL. We performed logistic regression and the predicted model was validated using both training and testing cohorts. RESULTS: Among patients, 31% and 12% experienced POUR lasting > 2 and > 4 days, respectively. Multivariable logistic model identified 6 predictors. For predicting POUR, internal validation using cross-validation approach showed good performance, with accuracy lasting > 2 (area under the curve [AUC] 0.73) and > 4 days (AUC 0.75). Split validation using pre-separated dataset also showed good performance, with accuracy lasting > 2 (AUC 0.73) and > 4 days (AUC 0.74). Calibration curves demonstrated that the model accurately predicted POUR lasting > 2 and > 4 days (from 0 to 80%). CONCLUSIONS: The proposed prediction model can assist clinicians in personalizing postoperative bladder care for patients undergoing prolapse surgery by providing accurate individual risk estimates.

ITDetect: a method to detect internal tandem duplication of FMS-like tyrosine kinase (FLT3) from next-generation sequencing data with high sensitivity and clinical application.

Internal tandem duplication (ITD) of the FMS-like tyrosine kinase (FLT3) gene is associated with poor clinical outcomes in patients with acute myeloid leukemia. Although recent methods for detecting FLT3-ITD from next-generation sequencing (NGS) data have replaced traditional ITD detection approaches such as conventional PCR or fragment analysis, their use in the clinical field is still limited and requires further information. Here, we introduce ITDetect, an efficient FLT3-ITD detection approach that uses NGS data. Our proposed method allows for more precise detection and provides more detailed information than existing in silico methods. Further, it enables FLT3-ITD detection from exome sequencing or targeted panel sequencing data, thereby improving its clinical application. We validated the performance of ITDetect using NGS-based and experimental ITD detection methods and successfully demonstrated that ITDetect provides the highest concordance with the experimental methods. The program and data underlying this study are available in a public repository.

Kernel-based hierarchical structural component models for pathway analysis.

MOTIVATION: Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. RESULTS: To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. AVAILABILITY AND IMPLEMENTATION: The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/, and the others will be shared on reasonable request to the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PATHOME-Drug: a subpathway-based polypharmacology drug-repositioning method.

MOTIVATION: Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. RESULTS: Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. AVAILABILITY AND IMPLEMENTATION: The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association study identifies novel susceptibilities to adult moyamoya disease.

Genome-wide association study has limited to discover single-nucleotide polymorphisms (SNPs) in several ethnicities. Here, we investigated an initial GWAS to identify genetic modifiers predicting with adult moyamoya disease (MMD) in Koreans. GWAS was performed in 216 patients with MMD and 296 controls using the large-scale Asian-specific Axiom Precision Medicine Research Array. A subsequent fine-mapping analysis was conducted to assess the causal variants associated with adult MMD. A total of 489,966 out of 802,688 SNPs were subjected to quality control analysis. Twenty-one SNPs reached a genome-wide significance threshold (p = 5 × 10-8) after pruning linkage disequilibrium (r2 < 0.8) and mis-clustered SNPs. Among these variants, the 17q25.3 region including TBC1D16, CCDC40, GAA, RNF213, and ENDOV genes was broadly associated with MMD (p = 3.1 × 10-20 to 4.2 × 10-8). Mutations in RNF213 including rs8082521 (Q1133K), rs10782008 (V1195M), rs9913636 (E1272Q), rs8074015 (D1331G), and rs9674961 (S2334N) showed a genome-wide significance (1.9 × 10-8 < p < 4.3 × 10-12) and were also replicated in the East-Asian populations. In subsequent analysis, RNF213 mutations were validated in a fine-mapping outcome (log10BF > 7). Most of the loci associated with MMD including 17q25.3 regions were detected with a statistical power greater than 80%. This study identifies several novel and known variations predicting adult MMD in Koreans. These findings may good biomarkers to evaluate MMD susceptibility and its clinical outcomes.

External validation of the de novo stress urinary incontinence prediction model after pelvic organ prolapse surgery in Korean women: a retrospective cohort study.

BACKGROUND: De novo stress urinary incontinence (SUI) may develop following pelvic organ prolapse surgery. Performing prophylactic continence surgery may reduce the risk of de novo SUI and subsequent continence surgery; however, it may increase the risk of complications. Therefore, many surgeons try to identify women at high risk for de novo SUI and perform continence surgery selectively. Recently, a model for predicting the risk of de novo SUI after prolapse surgery was developed using data from the Outcomes following vaginal Prolapse repair and midUrethral Sling (OPUS) trial; its prediction accuracy was significantly better than that of the stress test alone. However, few studies have verified its prediction accuracy in discrete populations. The aim of this study was to externally validate the prediction model for de novo SUI after prolapse surgery in Korean women. METHODS: This retrospective cohort study included 320 stress-continent women who underwent prolapse surgery for pelvic organ prolapse quantification stage 2-4 anterior or apical prolapse and who completed a 1-year follow-up. Predicted probabilities by the de novo SUI online risk calculator were compared with observed outcomes and quantitated using the model's area under the curve and calibration plot. Subgroup analyses were also performed by the type of prolapse surgery. RESULTS: The de novo SUI prediction model showed moderate discrimination in our study cohort; area under the curve (95% confidence interval) = 0.73 (0.67-0.78) in the whole cohort, 0.69 (0.61-0.78) in women who underwent native tissue repair or colpocleisis, and 0.74 (0.65-0.82) in those who underwent sacrocolpopexy. Calibration curves demonstrated that the model accurately predicted the observed outcomes of de novo SUI in women who underwent native tissue repair or colpocleisis but underestimated outcomes in those who underwent sacrocolpopexy. The predicted probability cutoff points corresponding to an actual risk of 50% were 40% in women who underwent native tissue repair or colpocleisis and 30% in those who underwent sacrocolpopexy. CONCLUSIONS: The de novo SUI prediction model is acceptable for use in Korean women and may aid in shared decision-making regarding prophylactic continence procedure at the time of prolapse surgery.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Consistent count region-copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data.

PURPOSE: Despite the importance of exonic copy number variations (CNVs) in human genetic diseases, reliable next-generation sequencing-based methods for detecting them are unavailable. We developed an expandable and robust exonic CNV detection tool called consistent count region (CCR)-CNV. METHODS: In total, about 1000 samples of the truth set were used for validating CCR-CNV. We compared CCR-CNV performance with 2 well-known CNV tools. Finally, to overcome the limitations of CCR-CNV, we devised a combined approach. RESULTS: The mean sensitivity and specificity of CCR-CNV alone were above 95%, which was superior to that of other CNV tools, such as DECoN and Atlas-CNV. However, low covered region and positive predictive value and high false discovery rate act as obstacles to its use in clinical settings. The combined approach showed much improved performance than CCR-CNV alone. CONCLUSION: In this study, we present a novel diagnostic tool that allows the identification of exonic CNVs with high confidence using various reagents and clinical next-generation sequencing platforms. We validated this method using the largest multiple ligation-dependent probe amplification-confirmed data set, including sufficient copy normal control data. The approach, combined with existing CNV tools, allows the implementation of CCR-CNV in clinical settings.

Development and validation of a prediction model for bothersome stress urinary incontinence after prolapse surgery: A retrospective cohort study.

OBJECTIVE: To develop and validate a prediction model for bothersome stress urinary incontinence after prolapse surgery and to compare it with an existing clinical prediction model (CUPIDO model). DESIGN: Retrospective cohort study. SETTING: Two tertiary hospitals in South Korea. POPULATION: A total of 1142 patients who underwent prolapse surgery with or without a concomitant midurethral sling. METHODS: To construct a prediction model, we performed logistic regression using both exhaustive and stepwise variable selection, validating the model both internally and externally. MAIN OUTCOME MEASURES: Bothersome stress urinary incontinence defined as the presence of bothersome symptoms of stress urinary incontinence and/or subsequent continence procedure one year after surgery. RESULTS: Postoperative bothersome stress urinary incontinence occurred in 10% of patients. A model containing six predictors (age, diabetes mellitus, subjective urinary incontinence, prolapse reduction stress test result, type of prolapse surgery, and a concomitant midurethral sling) showed excellent performance for predicting bothersome stress urinary incontinence (area under the curve 0.74, 95% confidence interval 0.62-0.86) and outperformed the CUPIDO model (area under the curve 0.63, 95% confidence interval 0.49-0.76; DeLong's test P = 0.014). CONCLUSIONS: This prediction model might be a useful tool to guide patient decision making regarding a concomitant continence procedure at the time of prolapse surgery. The predictive value of this model needs to be validated further in cohorts with different characteristics. TWEETABLE ABSTRACT: The proposed prediction model for bothersome stress urinary incontinence after prolapse surgery outperforms an existing model.

A practical approach towards causality mining in clinical text using active transfer learning.

OBJECTIVE: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge. METHODS: A practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text. RESULTS: The multi-model transfer learning technique when applied over multiple iterations, gains substantial performance improvements. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships. CONCLUSION: The presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well. SIGNIFICANCE: The presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

Self-reported pain scores as a predictor of preterm birth in symptomatic twin pregnancy: a retrospective study.

BACKGROUND: To evaluate the self-reported pain scores as a predictor of preterm birth (PTB) in symptomatic twin pregnancy and to develop a nomogram for the prediction model. METHODS: We conducted a retrospective study of 148 cases of symptomatic twin pregnancies before 34 weeks of gestation visited at Seoul national university hospital from 2013 to 2018. With other clinical factors, self-reported pain score was evaluated by the numerical rating scale (NRS) pain scores for pain intensity. By multivariate analyses and logistic regression, we developed a prediction model for PTB within 7 days. Using the Cox proportional hazards model, the curves were plotted to show the predictability of the PTB according to NRS pain score, while adjusting the other covariates. RESULTS: Twenty-three patients (15.5 %) delivered preterm within 7 days. By a logistic regression analysis, higher NRS pain score (OR 1.558, 95 % CI 1.093-2.221, P < 0.05), shorter cervical length (OR 3.164, 95 % CI 1.262-7.936, P < 0.05) and positive fibronectin results (OR 8.799, 95 % CI 1.101-70.330, P < 0.05) affect PTB within 7 days. Using the variables, the area under the receiver operating characteristic curve (AUROC) of the prediction model was 0.917. In addition, we developed a nomogram for the prediction of PTB within 7 days. CONCLUSIONS: Self-reported pain scores combined with cervical length and fetal fibronectin are useful in predicting impending PTB in symptomatic twin pregnancy.

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study.

BACKGROUND: Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services. As a result, many information resources have been created globally and are available for use. However, a defined structure to organize these resources into categories based on the nature and origin of the data is lacking. OBJECTIVE: This study aims to organize COVID-19 information resources into a well-defined structure to facilitate the easy identification of a resource, tracking information workflows, and to provide a guide for a contextual dashboard design and development. METHODS: A sequence of action research was performed that involved a review of COVID-19 efforts and initiatives on a global scale during the year 2020. Data were collected according to the defined structure of primary, secondary, and tertiary categories. Various techniques for descriptive statistical analysis were employed to gain insights into the data to help develop a conceptual framework to organize resources and track interactions between different resources. RESULTS: Investigating diverse information at the primary, secondary, and tertiary levels enabled us to develop a conceptual framework for COVID-19-related efforts and initiatives. The framework of resource categorization provides a gateway to access global initiatives with enriched metadata, and assists users in tracking the workflow of tertiary, secondary, and primary resources with relationships between various fragments of information. The results demonstrated mapping initiatives at the tertiary level to secondary level and then to the primary level to reach firsthand data, research, and trials. CONCLUSIONS: Adopting the proposed three-level structure allows for a consistent organization and management of existing COVID-19 knowledge resources and provides a roadmap for classifying future resources. This study is one of the earliest studies to introduce an infrastructure for locating and placing the right information at the right place. By implementing the proposed framework according to the stated guidelines, this study allows for the development of applications such as interactive dashboards to facilitate the contextual identification and tracking of interdependent COVID-19 knowledge resources.

"Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images".

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians.

RSK2 phosphorylates T-bet to attenuate colon cancer metastasis and growth.

Metastasis is a major cause of cancer-related deaths. Approximately 80% of patients with colorectal cancer develop liver metastasis and 20% develop lung metastasis. We found that at different stages of colon cancer, IFNγ secretion from peripheral blood mononuclear cells was decreased compared with healthy controls. The ribosomal S6 kinase (RSK) family of kinases has multiple cellular functions, and we examined their roles in this observed IFNγ decrease. Flow cytometry analysis of wild-type (WT) and RSK2 knockout (KO) mice revealed significantly lower levels of IFNγ in the RSK2 KO mice compared with the WT mice. Since IFNγ is a component of immunity, which contributes to protection against metastatic carcinomas, we conducted a colon cancer liver metastasis experiment. We found significantly greater metastasis in RSK2 KO mice compared with WT mice. Transcription factor T-bet can directly activate Ifnγ gene transcription. In vitro kinase assay results showed that RSK2 phosphorylated T-bet at serines 498 and 502. We show that phosphorylation of T-bet by RSK2 is required for IFNγ expression, because knockdown of RSK2 expression or overexpression of mutant T-bet reduces IFNγ mRNA expression. To verify the function of the phosphorylation sites, we overexpressed a constitutively active mutant T-bet (S498E/S502E) in bone marrow. Mutant T-bet restored the IFNγ mRNA levels and dramatically reduced the metastasis rate in these mice. Overall, these results indicate that phosphorylation of T-bet is required for the inhibition of colon cancer metastasis and growth through a positive regulation of RSK2/T-bet/IFNγ signaling.

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition.

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions.

Gene-environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene-Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene-alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

ONETOOL for the analysis of family-based big data.

Motivation: Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data. Results: ONETOOL implements the properties of well-known existing family data analysis tools and recently developed methods in a computationally efficient manner, and so is suitable for analyzing the vast amount of variant data available from sequencing family members, providing a rich choice of analysis methods for big data on families. Availability and implementation: ONETOOL is freely available from http://healthstat.snu.ac.kr/software/onetool/. Supplementary information: Supplementary data are available at Bioinformatics online.

Impact of laparoscopic radical hysterectomy on survival outcome in patients with FIGO stage IB cervical cancer: A matching study of two institutional hospitals in Korea.

OBJECTIVE: To compare survival outcomes of primary laparoscopic radical hysterectomy (LRH) and open radical hysterectomy (ORH) in patients with FIGO stage IB cervical cancer. METHODS: We retrospectively identified stage IB1-IB2 cervical cancer patients who received either LRH (n = 343) or ORH (n = 222) at two tertiary institutional hospitals between 2000 and 2018. To adjust for confounders, we conducted Mahalanobis distance-based sample matching for stage, histology, cervical mass size, parametrial invasion, and lymph node metastasis. Then, survival outcomes were compared between the matched groups. Through the independent matching processes, we narrowed the study population to stage IB1 patients and stage IB1 patients with tumor size ≤2 cm on pre-operative MRI. RESULTS: After matching, LRH group showed poorer progression-free survival (PFS) than ORH group (3-year: 85.4% vs. 91.8%; P = 0.036), whereas no significant difference in overall survival (OS) was found. Regarding recurrence patterns, no significant differences in the incidences of pelvic, retroperitoneal lymph node and abdominal recurrences, or distant metastasis were observed between the two groups. Among the matched patients with stage IB1 who had cervical mass size ≤2 cm, the LRH and ORH groups showed similar PFS (3-year: 90.0% vs. 93.1%; P = 0.8) and OS (5-year: 98.6% vs. 96.4%; P = 0.6). CONCLUSIONS: Despite the retrospective design, our matched cohort study suggests that ORH might be preferable for the surgical treatment of FIGO stage IB cervical cancer. However, in stage IB1 patients with tumor size ≤2 cm, LRH might be applicable, as equivalent outcomes were found regardless of the surgical approach. Further prospective studies are warranted.