scholarly journals Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Long ◽  
Chaofeng Liang ◽  
Xi’an Zhang ◽  
Luxiong Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Prediction Model ◽  
Basic Science ◽  
Prediction Accuracy ◽  
Genetic Basis ◽  
Bioinformatics Analysis ◽  
Clinical Treatment ◽  
Structural Level ◽  
Biotechnological Application ◽  
Key Genes

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

2020 ◽  
Vol 52 (8) ◽  
pp. 853-863
Author(s):  
Wenxin Zhai ◽  
Haijiao Lu ◽  
Shenghua Dong ◽  
Jing Fang ◽  
Zhuang Yu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein–protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

scholarly journals Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

2022 ◽  
Vol 12 (2) ◽  
pp. 757
Author(s):  
Xiaofeng Wang ◽  
Baochang Liu ◽  
Jiaqi Yun ◽  
Xueqi Wang ◽  
Haoliang Bai
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Thermal Deformation ◽  
Heating Temperature ◽  
Cooling Water ◽  
Interference Fit ◽  
Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

scholarly journals Screening and verifying key genes with poor prognosis in colon cancer through bioinformatics analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 6720-6732
Author(s):  
Buyuan Dong ◽  
Mengyu Chai ◽  
Hao Chen ◽  
Qian Feng ◽  
Rong Jin ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

2019 ◽  
Author(s):  
Gong‑Peng Dai ◽  
Li‑Ping Wang ◽  
Yu‑Qing Wen ◽  
Xue‑Qun Ren ◽  
Shu‑Guang Zuo
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Cancer Prognosis ◽  
Key Genes

scholarly journals RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Lu ◽  
John Panneerselvam ◽  
Lu Liu ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Service Providers ◽  
Cloud Service ◽  
Backpropagation Neural Network ◽  
Environmental Implications ◽  
Cloud Service Providers ◽  
Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

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