scholarly journals Ovarian Cancer Prediction Using PCA, K-PCA, ICA and Random Forest

2021 ◽  
pp. 103-108
Author(s):  
Asiye Sahin ◽  
Nermin Ozcan ◽  
Gokhan Nur
Keyword(s):  
Ovarian Cancer ◽  
Dimension Reduction ◽  
Large Scale ◽  
Time Saving ◽  
Data Set ◽  
Cancer Prediction ◽  
Reduction Methods ◽  
Advanced Stages ◽  
Model Training ◽  
Post Menopausal

Ovarian cancer, which is the most common in women and occurs mostly in the post-menopausal period, develops with the uncontrolled proliferation of the cells in the ovaries and the formation of tumors. Early diagnosis is very difficult and in most cases, it is a type of cancer that is in advanced stages when first diagnosed. While it tends to be treated successfully in the early stages where it is confined to the ovary, it is more difficult to treat in the advanced stages and is often fatal. For this reason, it has been focused on studies that predict whether people have ovarian cancer. In our study, we designed a RF-based ovarian cancer prediction model using a data set consisting of 49 features including blood routine tests, general chemistry tests and tumor marker data of 349 real patients. Since the data set containing too many dimensions will increase the time and resources that need to be spent, we reduced the dimension of the data with PCA, K-PCA and ICA methods and examined its effect on the result and time saving. The best result was obtained with a score of 0.895 F1 by using the new smaller-sized data obtained by the PCA method, in which the dimension was reduced from 49 to 6, in the RF method, and the training of the model took 18.191 seconds. This result was both better as a success and more economical in terms of time spent during model training compared to the prediction made over larger data with 49 features, where no dimension reduction method was used. The study has shown that in predictions made with machine learning models over large-scale medical data, dimension reduction methods will provide advantages in terms of time and resources by improving the prediction results.

scholarly journals A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 30
Author(s):  
Linhuai Tang ◽  
Zhihong Huang ◽  
Gang Cai ◽  
Yong Zheng ◽  
Jiamin Chen
Keyword(s):  
Binary Tree ◽  
High Performance ◽  
Large Scale ◽  
Partial Result ◽  
Data Set ◽  
Reduction Methods ◽  
Multiple Data Sets ◽  
Tree Path ◽  
Scale Matrix ◽  
Path Partition

Due to high parallelism, field-programmable gate arrays are widely used as accelerators in engineering and scientific fields, which involve a large number of operations of vector and matrix. High-performance accumulation circuits are the key to large-scale matrix operations. By selecting the adder as the reduction operator, the reduction circuit can implement the accumulation function. However, the pipelined adder will bring challenges to the design of the reduction circuit. To solve this problem, we propose a novel reduction circuit based on binary tree path partition, which can simultaneously handle multiple data sets with arbitrary lengths. It divides the input data into multiple groups and sends them to different iterations for calculation. The elements belonging to the same data set in each group are added to obtain a partial result, and the partial results of the same data set are added to achieve the final result. Compared with other reduction methods, it has the least area-time product.

Hrd in ovarian cancer: Defined today, evolving for the future.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18052-e18052
Author(s):  
Markus Eckstein ◽  
Kenneth Joel Bloom ◽  
Peter Riccelli ◽  
Frank Policht ◽  
Derry Mae Keeling ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Genomic Instability ◽  
Large Scale ◽  
Gene Mutations ◽  
Therapy Response ◽  
State Transitions ◽  
Data Set ◽  
Gene Target ◽  
Increased Risk

e18052 Background: Homologous Recombination Repair (HRR) gene mutations result in Homologous Recombination Deficiency (HRD) associated with increased risk of high grade serous ovarian (HGOC) cancer and subsequent response to PARP inhibitors (PARPi). Traditionally, HRD has been determined by testing for germline and/or somatic BRCA1/2 mutations. Today, a growing number of HRR gene mutations are known to result in HRD and genomic instability, thus being a suitable target for PARPi. Therapy response to PARPi is highest in BRCA-mutant followed by HRD+/non-BRCA-mutant HGOC. Today, no standard HRD testing methods exist, causing confusion for physicians, and leading to poor outcomes for missed PARPi eligible patients. Thus, there is need to understand HRD testing utilization and methods in HGOC to inform best practices and optimize HRD testing in the clinic. Methods: We assessed the testing landscape for determining HRD status in ovarian cancer using a data set of 8,400 newly diagnosed and metastatic ovarian cancer patients in the US from Q3-2018 through Q2-2019 identified from Diaceutics’ proprietary Global Diagnostic Index (GDI). Analysis of real-world BRCA1/2 and NGS associated testing data and laboratory profile mapping exercise of 82 US labs was carried out using Diaceutics proprietary methods and data sources to evaluate BRCA1/2 and/or HRD germline/somatic testing rates, test availability, and test panel HRR gene composition. Results: Overall, germline mutation testing rates were 3x greater than somatic testing rates. Excluding BRCA1/2, 67 labs offered comprehensive solid tumor NGS panels capable of measuring HRD with varied HRR gene target composition. Across 34 labs, 5 HRR genes were commonly found on panels: PALB2, ATM, BARD1, BRIP1 and CHEK2. 3 labs currently offering panels explicitly intended for HRD determination only include BRCA1/2 and at least one genomic instability marker (loss of heterozygosity, large-scale state transitions or telomeric allelic imbalance). Conclusions: Lack of standardized HRD panels and low testing rate identifying patients with somatic mutations in BRCA1/2 and other HRR genes is leading to poorer outcomes for missed patients eligible for PARPi’s. As clinical evidence linking HRD status with PARPi efficacy grows in ovarian as well as prostate and pancreatic cancer, Diaceutics recommends organizations such as ASCO, CAP or AMP establish defined universal HRD testing panels including relevant somatic/germline HRR genes and BRCA1/2 as well as genomic instability markers and educate stake holders aiding harmonization and ultimately, better treatment outcomes.

scholarly journals Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

2019 ◽  
Vol 8 (3) ◽  
pp. 139 ◽  
Author(s):  
Ugur Alganci
Keyword(s):  
Land Cover ◽  
Dimension Reduction ◽  
Comparative Evaluation ◽  
Support Vector ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Data Set ◽  
Multi Temporal ◽  
Reduction Methods

Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

The emerging roles of extracellular vesicles in ovarian cancer

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.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals The Prognostic and Predictive Role of Somatic BRCA Mutations in Ovarian Cancer: Results from a Multicenter Cohort Study

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 565
Author(s):  
Angela Toss ◽  
Claudia Piombino ◽  
Elena Tenedini ◽  
Alessandra Bologna ◽  
Elisa Gasparini ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Positive Impact ◽  
Progression Free Survival ◽  
Molecular Testing ◽  
Wild Type ◽  
Brca Mutations ◽  
Multicenter Cohort Study ◽  
Advanced Stages ◽  
Predictive Role

Previous research involving epithelial ovarian cancer patients showed that, compared to germline BRCA (gBRCA) mutations, somatic BRCA (sBRCA) mutations present a similar positive impact with regard to overall survival (OS) and platinum and PARP (poly (ADP-ribose) polymerase) inhibitor sensitivity. Nevertheless, molecular testing in these studies did not include copy number variation (CNV) analyses of BRCA genes. The aim of this study was to explore the prognostic and predictive role of sBRCA mutations as compared to gBRCA mutations in patients who were also tested for CNVs. Among the 158 patients included in the study, 17.09% of patients carried a pathogenic or likely pathogenic gBRCA variant and 15.19% of patients presented pathogenetic or likely pathogenic sBRCA variants and/or CNVs. Overall, 81.6% of the patients included in this study were diagnosed with a serous histotype, and 77.2% were in advanced stages. Among women diagnosed in advanced stages, gBRCA patients showed better progression-free survival and OS as compared to sBRCA and wild-type patients, whereas sBRCA patients did not show any advantage in outcome as compared to wild-type patients. In this study, the introduction of CNV analyses increased the detection rate of sBRCA mutations, and the resulting classification among gBRCA, sBRCA and wild-type patients was able to properly stratify the prognosis of OC patients. Particularly, sBRCA mutation patients failed to show any outcome advantage as compared to wild-type patients.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

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