scholarly journals An Entropy-based Directed Random Walk for Pathway Activity Inference Using Topological Importance and Gene Interactions

2021 ◽  
Author(s):  
Hui Xin Tay ◽  
Tole Sutikno ◽  
Shahreen Kasim ◽  
Shahreen Farhan Md Kasim ◽  
Shahliza Abd Halim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Random Walk ◽  
Directed Graph ◽  
Pathological Condition ◽  
Direct Proof ◽  
Superior Performance ◽  
Test Statistic ◽  
Entropy Weight ◽  
Weighting Method ◽  
Pathway Activity

The integration of microarray technologies and machine learning methods has become popular in predicting pathological condition of diseases and discovering risk genes. The traditional microarray analysis considers pathways as simple gene sets, treating all genes in the pathway identically while ignoring the pathway network's structure information. This study, however, proposed an entropy-based directed random walk (e-DRW) method to infer pathway activity. This study aims (1) To enhance the gene-weighting method in Directed Random Walk (DRW) by incorporating t-test statistic scores and correlation coefficient values, (2) To implement entropy as a parameter variable for random walking in a biological network, and (3) To apply Entropy Weight Method (EWM) in DRW pathway activity inference. To test the objectives, the gene expression dataset was used as input datasets while the pathway dataset was used as reference datasets to build a directed graph. An equation was proposed to assess the connectivity of nodes in the directed graph via probability values calculated from the Shannon entropy formula. A direct proof of calculation based on the proposed mathematical formula was presented using e-DRW with gene expression data. Based on the results, there was an improvement in terms of sensitivity of prediction and accuracy of cancer classification between e-DRW and conventional DRW. The within-dataset experiments indicated that our novel method demonstrated robust and superior performance in terms of accuracy and number of predicted risk-active pathways compared to the other DRW methods. In conclusion, the results revealed that e-DRW not only improved prediction performance, but also effectively extracted topologically important pathways and genes that are specifically related to the corresponding cancer types.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

Fuzzy Synthetic Evaluation of Power Quality Based on Combination Weighting Method

2011 ◽  
Vol 201-203 ◽  
pp. 2470-2475
Author(s):  
Yuan Sheng Huang ◽  
Li Ming Yuan
Keyword(s):  
Power Quality ◽  
Fuzzy Theory ◽  
Evaluation Model ◽  
Weight Coefficient ◽  
National Standard ◽  
Fuzzy Synthetic Evaluation ◽  
Entropy Weight ◽  
Weighting Method ◽  
Evaluation Indexes ◽  
Synthetic Evaluation

According to the national standard, this paper presents the evaluation indexes of power quality and the classifications of each index. The method integrates advantages of both G1 and entropy weight coefficient method. Also, it establishes an fuzzy synthetic evaluation for power quality evaluation by fuzzy theory. 5 observation points on the power quality was graded. The test shows that the combination weighting evaluation model based on fuzzy synthetic evaluation can evaluate the power quality comprehensively and effectively.

VP90 Which Matching Adjusted Indirect Comparison Method Is Best?

2019 ◽  
Vol 35 (S1) ◽  
pp. 94-95
Author(s):  
Jonathan Alsop ◽  
Lawrence Pont ◽  
Martin Scott
Keyword(s):  
Treatment Effectiveness ◽  
Indirect Comparison ◽  
Patient Characteristics ◽  
Comparison Method ◽  
Ease Of Use ◽  
Superior Performance ◽  
Weighting Method ◽  
Entropy Balancing ◽  
Level Data ◽  
Adjusted Indirect Comparison

IntroductionMatching adjusted indirect comparison (MAIC) methods are extremely useful when conducting ITCs, as they reduce baseline imbalances between studies, particularly upon patient characteristics that are confounded with treatment. The standard approach when conducting MAIC is that proposed by Signorovitch et al. (2010). However, there are newer, and potentially better, methods available.MethodsThree different MAIC methods (Signorovitch, Entropy Balancing, Polynomial Weighting) were compared using multiple phase 3 RCTs conducted in Diabetic Retinal Edema. The matching ability of each method was assessed, alongside its ability to avoid large weights (i.e. avoiding high leverage), and maximise effective same size (ESS). Each method's overall ease of use and impact upon estimates of treatment effectiveness were also evaluated.ResultsAll methods were able to precisely match the aggregate level data. However, the Entropy Balancing and Polynomial Weighting both outperformed the Signorovitch method in terms of having the lowest maximum weights. The Polynomial Weighting provided the highest ESS. The Entropy Balancing method was arguably the most challenging to implement, whilst the Signorovitch method the least. The Polynomial Weighting method appears to provide the greatest flexibility to the user.ConclusionsWhilst the Signorovitch method has become almost synonymous with MAIC, the Entropy Balancing and Polynomial Weighting methods offer potentially superior performance. In the absence of head-to-head trial data, these new MAIC approaches should provide less biased and more precise estimates of comparative effectiveness – ultimately leading to better decision making by regulators and payers.

Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection

2004 ◽  
Vol 3 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Minhui Paik ◽  
Yuhong Yang
Keyword(s):  
Gene Expression ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Weighting Method ◽  
Considerable Uncertainty ◽  
Combined Classifier ◽  
Nearest Neighbor Classifiers

Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when there is considerable uncertainty in choosing the best candidate classifier. As an alternative to selecting a single “winner," we propose a weighting method to combine the multiple NN rules. Four gene expression data sets are used to compare its performance with CV methods. The results show that when the CV selection is unstable, the combined classifier performs much better.

scholarly journals SDGs-Based River Health Assessment for Small- and Medium-Sized Watersheds

2020 ◽  
Vol 12 (5) ◽  
pp. 1846
Author(s):  
Chenyang Xue ◽  
Chaofeng Shao ◽  
Sihan Chen
Keyword(s):  
Sustainable Development ◽  
Index System ◽  
Health Assessment ◽  
Oxygen Demand ◽  
Research Area ◽  
Entropy Weight ◽  
Frequency Method ◽  
River Health ◽  
Element Analysis ◽  
Weighting Method

A river health assessment index system was established, focusing on the realistic needs of county sustainable development and the refined management of small- and medium-sized watersheds. The index system takes into consideration the United Nations’ Sustainable Development Goals (SDGs) and the vulnerability characteristics of small- and medium-sized watershed ecosystems and consists of 15 indicators in four areas: clean water, sanitation, the present status of biodiversity and threats to biodiversity. This paper uses the minimum discrimination information principle to construct a dynamic combination-weighting technology composed of a subjective weighting method (document frequency method) and an objective weighting method (entropy weight method). Using the fuzzy matter-element analysis theory, a comprehensive river health assessment technology system was constructed. Baoxing County was chosen as the research area and the results reveal that: (1) Key indicators are the biodiversity index of fish, water use intensity, endemic or indicative species retention, and chemical oxygen demand (COD) emissions. (2) The Euclid approach degree of Baoxing County indicates that the entire river is in a moderate state of health. In the future, towns must take targeted measures to coordinate the relationship between the ecological environment and socio-economic development, and enhancement and releasing must be prioritised.

scholarly journals The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models

2020 ◽  
Vol 13 (3) ◽  
pp. 48 ◽  
Author(s):  
Yuchen Zhang ◽  
Shigeyuki Hamori
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Walk ◽  
Exchange Rates ◽  
Price Index ◽  
Economic Models ◽  
Superior Performance ◽  
Support Vector ◽  
Out Of Sample ◽  
Modern Machine

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical models can ever outperform the random walk in forecasting out-of-sample exchange rates has received scholarly attention. Recently, a combination of fundamental models with machine learning methodologies was found to outcompete the predictability of random walk (Amat et al. 2018). This paper focuses on combining modern machine learning methodologies with traditional economic models and examines whether such combinations can outperform the prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the monetary model, and the Taylor rule models). We performed a thorough robustness check using six government bonds with different maturities and four price indexes, which demonstrated the superior performance of fundamental models combined with modern machine learning in predicting future exchange rates in comparison with the results of random walk. These results were examined using a root mean squared error (RMSE) and a Diebold–Mariano (DM) test. The main findings are as follows. First, when comparing the performance of fundamental models combined with machine learning with the performance of random walk, the RMSE results show that the fundamental models with machine learning outperform the random walk. In the DM test, the results are mixed as most of the results show significantly different predictive accuracies compared with the random walk. Second, when comparing the performance of fundamental models combined with machine learning, the models using the producer price index (PPI) consistently show good predictability. Meanwhile, the consumer price index (CPI) appears to be comparatively poor in predicting exchange rate, based on its poor results in the RMSE test and the DM test.

scholarly journals Safety Evaluation Method of Antifloating Anchor System Based on Comprehensive Weighting Method and Gray-Fuzzy Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Jingwen ◽  
Qiao Jiangang ◽  
Dou Yuanming ◽  
Fu Xu ◽  
Liu Xiaoli
Keyword(s):  
Risk Factors ◽  
Evaluation Method ◽  
Fuzzy Theory ◽  
Underground Structure ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Grey Theory ◽  
Entropy Weight ◽  
Weighting Method ◽  
Anchor System

The risk factors in the safety evaluation of antifloating anchor system of underground structure have the characteristics of complexity, grey, and fuzziness. Based on the Delphi method, analytic hierarchy process (AHP), and entropy weight method, this paper establishes a three-level evaluation index system based on four main risk factors and calculates the subjective and objective comprehensive weights of the index according to the comprehensive weighting method. It not only takes into account the valuable experience of the expert group but also reflects the objective impact of the subjective score on the system. On the basis of the above research, the grey-fuzzy safety evaluation method of antifloating anchor system is established by using the grey theory and the relevant theory of fuzzy mathematics. The reliability of the method is verified by an example, which has certain theoretical significance and application value.

122 SUPPRESSION OF EPIGENETIC MODIFIERS ALTERS THE BOVINE EMBRYONIC DEVELOPMENTAL PROGRAM DURING IN VITRO CULTURE

2014 ◽  
Vol 26 (1) ◽  
pp. 175 ◽  
Author(s):  
M. D. Snyder ◽  
J. H. Pryor ◽  
M. D. Peoples ◽  
G. L. Williamson ◽  
M. C. Golding ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Embryonic Development ◽  
Dna Methyltransferase ◽  
Test Statistic ◽  
Gene Target ◽  
Blastocyst Rate ◽  
Green Fluorescent ◽  
Fluorescent Dextran

During early bovine embryogenesis, the regular establishment of DNA methylation and histone modification patterns is essential for proper gene expression and continuation of embryonic development. Epigenome patterns established during this period, if improperly maintained, can lead to developmental anomalies and may partially explain the lower pregnancy rates of in vitro-produced embryos. We hypothesised that the suppression of translation of the genes euchromatic histone-lysine N-methyltransferase 2 (EHMT2), DNA methyltransferase 3A (DNMT3A), absent, small, or homeotic-like (ASH2L), and SET domain, bifurcated 1 (SETDB1) would provide insightful information on the importance of these genes during early embryonic development in an in vitro setting. In order to define the roles of these genes, small interfering RNA (siRNA) targeting the gene of interest were synthesised and target verified in bovine cell culture using quantitative real-time RT-PCR (RT-qPCR). We acquired matured bovine oocytes from commercial suppliers, followed by IVF by standard laboratory procedures. Eighteen hours post IVF, cumulus cells were removed and zygotes separated into 3 different treatment groups: non-injected controls (CNTL), non-targeting siRNA injected controls (siNULL), and injection with siRNA targeting the gene of interest (si “gene target”). Each siRNA was mixed with a green fluorescent dextran at a concentration of 20 μM and ~100 pL injected cytoplasmically. The green fluorescent dextran was used to give visual confirmation that zygotes were indeed injected. Post-injection, fluorescent embryos were separated and cultured in Bovine Evolve (Zentih Biotech) medium supplemented with 4 mg mL–1 of BSA (Probumin, Millipore). Cleavage rates were monitored on Day 2, and only cleaved embryos were cultured further. On Day 8 post-IVF, embryos were morphologically examined and numbers of blastocysts recorded. Mean development rates between siNULL and targeting siRNA were compared using a t-test statistic. Over the course of these experiments the mean blastocyst rate for CNTL zygotes was 34.5% ± 2.6 s.e.m. (n = 1647). None of the zygotes injected with siEHMT2 (n = 1184) or siSETDB1 (n = 361) reached the blastocyst stage and these rates differed from the siNULL rate (21.0% ± 2.5 s.e.m., n = 1587; P < 0.05). Morphologically, embryos from both groups developed to the morula stage before they exhibited fragmentation. Injection of siDNMT3A also resulted in significant loss of viability at the 8-cell stage and few zygotes injected (n = 1057) developed to blastocyst (2.1% ± 0.5 s.e.m.; P < 0.001). Inhibiting gene expression of ASH2L showed little variation in blastocyst rate from our siNULL embryos (31.3% ± 2.0 s.e.m., n = 466 v. 34.8% ± 1.9 s.e.m., n = 418, respectively, P > 0.2). It is unknown at this time if inhibition of ASH2L translation will have effects later in development. Ongoing experiments analysing DNA methylation and histone modifications through immunocytochemistry and global gene expression via RT-qPCR will further explore the establishment and maintenance of these genes in the embryonic epigenome.

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