Prediction of Ovarian Cancer-Related Metabolites Based on Graph Neural Network

Ovarian cancer is one of the three most malignant tumors of the female reproductive system. At present, researchers do not know its pathogenesis, which makes the treatment effect unsatisfactory. Metabolomics is closely related to drug efficacy, safety evaluation, mechanism of action, and rational drug use. Therefore, identifying ovarian cancer-related metabolites could greatly help researchers understand the pathogenesis and develop treatment plans. However, the measurement of metabolites is inaccurate and greatly affects the environment, and biological experiment is time-consuming and costly. Therefore, researchers tend to use computational methods to identify disease-related metabolites in large scale. Since the hypothesis that similar diseases are related to similar metabolites is widely accepted, in this paper, we built both disease similarity network and metabolite similarity network and used graph convolutional network (GCN) to encode these networks. Then, support vector machine (SVM) was used to identify whether a metabolite is related to ovarian cancer. The experiment results show that the AUC and AUPR of our method are 0.92 and 0.81, respectively. Finally, we proposed an effective method to prioritize ovarian cancer-related metabolites in large scale.

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Identification of KIF23 as a prognostic signature for ovarian cancer based on large scale samples and clinical validation

10.21203/rs.2.23036/v1 ◽

2020 ◽

Author(s):

Yuexin Hu ◽

Mingjun Zheng ◽

Caixia Wang ◽

Shuang Wang ◽

Rui Gou ◽

...

Keyword(s):

Ovarian Cancer ◽

Overall Survival ◽

Cancer Patients ◽

Poor Prognosis ◽

Large Scale ◽

Cox Regression ◽

Malignant Tumors ◽

Chemotherapy Resistance ◽

Survival Prognosis ◽

Molecular Targeted Drug

Abstract Background: Ovarian cancer is one of the common malignant tumors in gynecology. Although the treatment strategy for ovarian cancer has been greatly improved in recent years, due to the metastasis, recurrence and drug resistance, the 5-year overall survival rate of patients is still less than 47%. However, at present, there is no specific markers for clinical application. The purpose of this study is to verify the expression and clinical significance of KIF23 in ovarian cancer and identify potential targets for the clinical treatment of ovarian cancer. Methods: The expression of KIF23 in ovarian cancer tissues and its relationship between survival prognosis and clinical pathological parameters were analyzed in Oncomine, GEO, and TCGA databases. KIF23 expression was analyzed by Kaplan-Meier plotter database and its relationship with chemo-resistance was studied. The molecular mechanism involved in KIF23 was analyzed from the perspective of gene mutation, copy number variation and other genomics. Finally, immunohistochemistry experiment was used to verify the expression of KIF2, and its relationship between the clinical pathological parameters and prognosis of ovarian cancer patients was analyzed by single factor and multivariate Cox regression models. Results: Bioinformatic and experimental results have demonstrated that KIF23 is highly expressed in ovarian cancer, and its high expression is positively correlated with poor prognosis. Overexpression of KIF23 can cause chemotherapy resistance in ovarian cancer and affect the overall survival of patients. Genomics analysis showed that KIF23 expression was associated with mutations such as FLG2 and TTN, and it was significantly enriched in tumor signaling pathways such as DNA replication and cell cycle. Conclusions: KIF23 can not only be used as a biomarker of poor prognosis in patients with various stages of ovarian cancer, but also be used as a molecular targeted drug and an independent prognostic biomarker for the treatment of ovarian cancer patients.

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Multi-Temporal SAR Data Large-Scale Crop Mapping Based on U-Net Model

Remote Sensing ◽

10.3390/rs11010068 ◽

2019 ◽

Vol 11 (1) ◽

pp. 68 ◽

Cited By ~ 25

Author(s):

Sisi Wei ◽

Hong Zhang ◽

Chao Wang ◽

Yuanyuan Wang ◽

Lu Xu

Keyword(s):

Large Scale ◽

Structural Information ◽

Mapping Method ◽

Support Vector ◽

Smallholder Farming ◽

Convolutional Network ◽

Multi Temporal ◽

Crop Mapping ◽

Sar Data ◽

Crop Planting

Due to the unique advantages of microwave detection, such as its low restriction from the atmosphere and its capability to obtain structural information about ground targets, synthetic aperture radar (SAR) is increasingly used in agricultural observations. However, while SAR data has shown great potential for large-scale crop mapping, there have been few studies on the use of SAR images for large-scale multispecies crop classification at present. In this paper, a large-scale crop mapping method using multi-temporal dual-polarization SAR data was proposed. To reduce multi-temporal SAR data redundancy, a multi-temporal images optimization method based on analysis of variance (ANOVA) and Jeffries–Matusita (J–M) distance was applied to the time series of images after preprocessing to select the optimal images. Facing the challenges from smallholder farming modes, which caused the complex crop planting patterns in the study area, U-Net, an improved fully convolutional network (FCN), was used to predict the different crop types. In addition, the batch normalization (BN) algorithm was introduced to the U-Net model to solve the problem of a large number of crops and unbalanced sample numbers, which had greatly improved the efficiency of network training. Finally, we conducted experiments using multi-temporal Sentinel-1 data from Fuyu City, Jilin Province, China in 2017, and we obtained crop mapping results with an overall accuracy of 85% as well as a Kappa coefficient of 0.82. Compared with the traditional machine learning methods (e.g., random forest (RF) and support vector machine (SVM)), the proposed method can still achieve better classification performance under the condition of a complex crop planting structure.

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ROLE OF ULTRASOUND IN THE DETECTION OF RECURRENT OVARIAN CANCER

UZBEK MEDICAL JOURNAL ◽

10.26739/2181-0664-2020-6-4 ◽

2020 ◽

Vol 6 (1) ◽

pp. 23-31

Author(s):

M. Alisherova ◽

◽

M. Ismailova

Keyword(s):

Ovarian Cancer ◽

Surgical Treatment ◽

Reproductive System ◽

Malignant Tumors ◽

Recurrent Ovarian Cancer ◽

Primary Diagnosis ◽

Female Reproductive System

Currently, there are no standard approaches to monitoring patients with ovarian cancer (OC). While the role of ultrasound (US) has been identified in the primary diagnosis of OS, it is still controversial during the subsequent surgical treatment of OC. In world statistics, ovarian cancer is consistently among the four main localizations of malignant tumors of the female reproductive system, along with tumors of the breast, body and cervix.

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A prospective observational study on the effectiveness of risk of malignancy index score (RMI 3) in a tertiary care centre in Eastern India

Asian Journal of Medical Sciences ◽

10.3126/ajms.v11i5.29548 ◽

2020 ◽

Vol 11 (5) ◽

pp. 54-60

Author(s):

Apurba Mandal ◽

Shibram Chattopadhyay ◽

Sushanta Mondal ◽

Arunava Biswas

Keyword(s):

Ovarian Cancer ◽

Adnexal Mass ◽

Malignant Tumors ◽

Histopathological Examination ◽

Tertiary Care ◽

Ca 125 ◽

Tertiary Care Centre ◽

Gynecology And Obstetrics ◽

Risk Of Malignancy ◽

Malignant Lesions

Background: Adnexal mass is a common presentation in today’s gynecological practice. The incidence of ovarian cancer is increasing day by day and diagnosis is often difficult to be made pre operatively with inadequate surgical exploration is a regular occurrence. Aims and Objectives: To assess and validate the importance of RMI-3 score as pre-operative diagnostic tool of differentiating benign from malignant adnexal mass for starting first line therapy of ovarian cancer and to find out the incidences of ovarian malignancy among study population. Material and Methods: The study was conducted in the Department of Gynecology and Obstetrics on (n=115) patients attending GOPD and indoor with adnexal mass fulfilling the inclusion and exclusion criteria using purposive sampling technique. All the selected cases underwent ultrasonography and serum CA- 125 level estimation necessary for calculating RMI score. A score of >200 was taken as suggestive of malignancy and confirmatory diagnosis was performed by histopathological examination obtained from staging laparotomy of adnexal mass. The individual scores were then correlated with final outcomes with statistical analyses. Results: The study revealed benign ovarian tumors are more under 50 years (78.46%) and patients with normal BMI are diagnosed with maximum of malignancy (n = 28). History of tubal ligation carried less risk of malignancy (p<0.0001). Histologically malignant tumors found mostly in 71.4% postmenopausal group whereas 94.1% benign pathology were present in perimenopausal group and there is no association found between parity and histopathology (p=0.058). Bilateral (p=0.013), multilocular (p=0.000) tumors with solid areas (p<0.0001) and thick papillary projections (p<0.0001) had statistically significant association with malignant lesions. RMI score (>200) had more efficacy than serum CA-125 level (>46) in differentiating malignant lesions from benign one in terms of specificity (96% vs 83.87%) and positive predictive value (95% vs 79.17%). Conclusions: RMI-3 score is a simple, reliable and effective tool in differentiating benign from malignant adnexal masses thereby help in quick referral and management of cases with increase chances of survival of the patients.

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The emerging roles of extracellular vesicles in ovarian cancer

Current Drug Metabolism ◽

10.2174/1389200221666201110155721 ◽

2020 ◽

Vol 21 ◽

Author(s):

Yin-xue Wang ◽

Yi-xiang Wang ◽

Yi-ke Li ◽

Shi-yan Tu ◽

Yi-qing Wang

Keyword(s):

Ovarian Cancer ◽

Extracellular Vesicles ◽

Large Scale ◽

Current Treatment ◽

Detection Methods ◽

Drug Resistant ◽

Apoptotic Bodies ◽

Biological Mechanisms ◽

Gynecological Malignancy ◽

Resistance To Chemotherapy

: Ovarian cancer (OC) is one of the deadliest gynecological malignancy. Epithelial ovarian cancer (EOC) is its most common form. OC has both a poor prognosis and a high mortality rate due to the difficulties of early diagnosis, the limitation of current treatment and resistance to chemotherapy. Extracellular vesicles is a heterogeneous group of cellderived submicron vesicles which can be detected in body fluids, and it can be classified into three main types including exosomes, micro-vesicles, and apoptotic bodies. Cancer cells can produce more EVs than healthy cells. Moreover, the contents of these EVs have been found distinct from each other. It has been considered that EVs shedding from tumor cells may be implicated in clinical applications. Such as a tool for tumor diagnosis, prognosis and potential treatment of certain cancers. In this review, we provide a brief description of EVs in diagnosis, prognosis, treatment, drug-resistant of OC. Cancer-related EVs show powerful influences on tumors by various biological mechanisms. However, the contents mentioned above remain in the laboratory stage and there is a lack of large-scale clinical trials, and the maturity of the purification and detection methods is a constraint. In addition, amplification of oncogenes on ecDNA is remarkably prevalent in cancer, it may be possible that ecDNA can be encapsulated in EVs and thus detected by us. In summary, much more research on EVs needs to be perform to reveal breakthroughs in OC and to accelerate the process of its application on clinic.

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NPU RGB+D Dataset and a Feature-Enhanced LSTM-DGCN Method for Action Recognition of Basketball Players

Applied Sciences ◽

10.3390/app11104426 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4426

Author(s):

Chunyan Ma ◽

Ji Fan ◽

Jinghao Yao ◽

Tao Zhang

Keyword(s):

Action Recognition ◽

Large Scale ◽

Short Term Memory ◽

Evaluation Criteria ◽

Image Data ◽

Basketball Player ◽

Basketball Players ◽

Convolutional Network ◽

Atomic Actions ◽

New Feature

Computer vision-based action recognition of basketball players in basketball training and competition has gradually become a research hotspot. However, owing to the complex technical action, diverse background, and limb occlusion, it remains a challenging task without effective solutions or public dataset benchmarks. In this study, we defined 32 kinds of atomic actions covering most of the complex actions for basketball players and built the dataset NPU RGB+D (a large scale dataset of basketball action recognition with RGB image data and Depth data captured in Northwestern Polytechnical University) for 12 kinds of actions of 10 professional basketball players with 2169 RGB+D videos and 75 thousand frames, including RGB frame sequences, depth maps, and skeleton coordinates. Through extracting the spatial features of the distances and angles between the joint points of basketball players, we created a new feature-enhanced skeleton-based method called LSTM-DGCN for basketball player action recognition based on the deep graph convolutional network (DGCN) and long short-term memory (LSTM) methods. Many advanced action recognition methods were evaluated on our dataset and compared with our proposed method. The experimental results show that the NPU RGB+D dataset is very competitive with the current action recognition algorithms and that our LSTM-DGCN outperforms the state-of-the-art action recognition methods in various evaluation criteria on our dataset. Our action classifications and this NPU RGB+D dataset are valuable for basketball player action recognition techniques. The feature-enhanced LSTM-DGCN has a more accurate action recognition effect, which improves the motion expression ability of the skeleton data.

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A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽

10.3390/cancers13092111 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2111

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Lun Hu ◽

Zhen-Hao Guo ◽

Lei Wang ◽

...

Keyword(s):

Drug Target ◽

Large Scale ◽

Computational Models ◽

Structural Information ◽

Characteristic Curve ◽

Representation Learning ◽

Graph Representation ◽

Convolutional Network ◽

Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽

10.3390/math9070772 ◽

2021 ◽

Vol 9 (7) ◽

pp. 772

Author(s):

Seonghun Kim ◽

Seockhun Bae ◽

Yinhua Piao ◽

Kyuri Jo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Drug Response ◽

Response Prediction ◽

Biological Data ◽

Expression Data ◽

Convolutional Network ◽

Essential Information ◽

Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

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Challenges and opportunities for ovarian cancer management in the epidemic of Covid-19: lessons learned from Wuhan, China

Journal of Ovarian Research ◽

10.1186/s13048-021-00784-2 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Zhilan Chen ◽

Chun Zhang ◽

Jiu Yin ◽

Xin Xin ◽

Hemei Li ◽

...

Keyword(s):

Ovarian Cancer ◽

Large Scale ◽

Treatment Plan ◽

Clinical Care ◽

Gynecologic Cancer ◽

Treatment Phase ◽

Tumor Stage ◽

Current Treatment ◽

Lessons Learned ◽

Cancer Management

AbstractChina and the rest of the world are experiencing an outbreak of the 2019 novel coronavirus disease (COVID-19). Patients with cancer are more susceptible to viral infection and are more likely to develop severe complications, as compared to healthy individuals. The growing spread of COVID-19 presents challenges for the clinical care of patients with gynecological malignancies. Ovarian debulking surgery combined with the frequent need for chemotherapy is most likely why ovarian cancer was rated as the gynecologic cancer most affected by COVID-19. Therefore, ovarian cancer presents a particular challenging task. Concerning the ovarian cancer studies with confirmed COVID-19 reported from large-scale general hospitals in Wuhan, we hold that the treatment plan was adjusted appropriately and an individualized remedy was implemented. The recommendations discussed here were developed mainly based on the experience from Wuhan. We advise that the management strategy for ovarian cancer patients should be adjusted in the light of the local epidemic situation and formulated according to the pathological type, tumor stage and the current treatment phase. Online medical service is an effective and convenient communication platform during the pandemic.

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Development and Validation of a Post-Earthquake Safety Assessment System for High-Rise Buildings Using Acceleration Measurements

Mathematics ◽

10.3390/math9151758 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1758

Author(s):

Koji Tsuchimoto ◽

Yasutaka Narazaki ◽

Billie F. Spencer

Keyword(s):

Safety Assessment ◽

Large Scale ◽

Model Updating ◽

Safety Evaluation ◽

Assessment System ◽

Shaking Table ◽

Structural Safety ◽

High Rise ◽

Steel Building ◽

Earthquake Safety

After a major seismic event, structural safety inspections by qualified experts are required prior to reoccupying a building and resuming operation. Such manual inspections are generally performed by teams of two or more experts and are time consuming, labor intensive, subjective in nature, and potentially put the lives of the inspectors in danger. The authors reported previously on the system for a rapid post-earthquake safety assessment of buildings using sparse acceleration data. The proposed framework was demonstrated using simulation of a five-story steel building modeled with three-dimensional nonlinear analysis subjected to historical earthquakes. The results confirmed the potential of the proposed approach for rapid safety evaluation of buildings after seismic events. However, experimental validation on large-scale structures is required prior to field implementation. Moreover, an extension to the assessment of high-rise buildings, such as those commonly used for residences and offices in modern cities, is needed. To this end, a 1/3-scale 18-story experimental steel building tested on the shaking table at E-Defense in Japan is considered. The importance of online model updating of the linear building model used to calculate the Damage Sensitive Features (DSFs) during the operation is also discussed. Experimental results confirm the efficacy of the proposed approach for rapid post-earthquake safety evaluation for high-rise buildings. Finally, a cost-benefit analysis with respect to the number of sensors used is presented.

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