scholarly journals DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hagai Levi ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Identification Algorithm ◽  
Module Identification ◽  
Reduced Rate

scholarly journals DiME: A Scalable Disease Module Identification Algorithm with Application to Glioma Progression

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e86693 ◽  
Author(s):  
Yunpeng Liu ◽  
Daniel A. Tennant ◽  
Zexuan Zhu ◽  
John K. Heath ◽  
Xin Yao ◽  
...  
Keyword(s):  
Identification Algorithm ◽  
Module Identification ◽  
Disease Module ◽  
Glioma Progression

scholarly journals A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Zhang ◽  
Zhengkui Lin ◽  
Xiaofeng Lin ◽  
Xue Zhang ◽  
Qian Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Significance ◽  
Identification Algorithm ◽  
Gene Module ◽  
Number Of Clusters ◽  
Module Identification ◽  
Gene Modules ◽  
Algorithm Framework ◽  
Key Genes

AbstractTo further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.

Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

2020 ◽  
Vol 48 (4) ◽  
pp. 287-314
Author(s):  
Yan Wang ◽  
Zhe Liu ◽  
Michael Kaliske ◽  
Yintao Wei
Keyword(s):  
Elastic Deformation ◽  
Estimation Algorithm ◽  
Force Sensor ◽  
Euler Method ◽  
Rolling Contact ◽  
Body Motion ◽  
Identification Algorithm ◽  
Active Perception ◽  
Kinematics Model ◽  
Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

Localization and Non-Localization Node Identification Using Dream in MANET

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Amit Singh ◽  
Nitin Mishra ◽  
Angad Singh
Keyword(s):  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Fundamental Problem ◽  
Identification Algorithm ◽  
Mobile Nodes ◽  
Position Information ◽  
Location Services ◽  
Wireless Router ◽  
Mobile Ad Hoc

 A Wireless Mobile Ad-hoc Network consists of variety of mobile nodes that temporally kind a dynamic infrastructure less network. To modify communication between nodes that don’t have direct radio contact, every node should operate as a wireless router and potential forward knowledge traffic of behalf of the opposite node. In MANET Localization is a fundamental problem. Current localization algorithm mainly focuses on checking the localizability of a network and/or how to localize as many nodes as possible. It could provide accurate position information foe kind of expanding application. Localization provide information about coverage, deployment, routing, location, services, target tracking and rescue If high mobility among the mobile nodes occurs path failure breaks. Hence the location information cannot be predicted. Here we have proposed a localization based algorithm which will help to provide information about the localized and non-localized nodes in a network. In the proposed approach DREAM protocol and AODV protocol are used to find the localizability of a node in a network. DREAM protocol is a location protocol which helps to find the location of a node in a network whereas AODV is a routing protocol it discover route as and when necessary it does not maintain route from every node to every other. To locate the mobile nodes in a n/w an node identification algorithm is used. With the help of this algorithm localized and non-localized node can be easily detected in respect of radio range. This method helps to improve the performance of a module and minimize the location error and achieves improved performance in the form of UDP packet loss, received packet and transmitted packets, throughput, routing overhead, packet delivery fraction. All the simulation done through the NS-2 module and tested the mobile ad-hoc network.

An Efficient Genuine-Video Identification Algorithm: TDIA

2010 ◽  
Vol 33 (1) ◽  
pp. 175-183 ◽  
Author(s):  
Guo-Rui ZHOU ◽  
Wen-Jiang WANG ◽  
Shi-Xin SUN
Keyword(s):  
Identification Algorithm ◽  
Video Identification

Target Localization and Identification Algorithm for Ultra Wideband Through-wall Radar

2010 ◽  
Vol 32 (11) ◽  
pp. 2624-2629 ◽  
Author(s):  
Shi-you Wu ◽  
Qiong Huang ◽  
Jie Chen ◽  
Sheng-wei Meng ◽  
Guang-you Fang ◽  
...  
Keyword(s):  
Target Localization ◽  
Ultra Wideband ◽  
Identification Algorithm
Sign in / Sign up

Export Citation Format

Share Document