Large-scale on-line data processing systems

1961 ◽  
Vol 80 (10) ◽  
pp. 767-772
Author(s):  
S. Levine
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
On Line

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

On-Line Documentation Systems: History and Issues

1983 ◽  
Vol 27 (13) ◽  
pp. 1027-1030
Author(s):  
Joseph J. Limanowski
Keyword(s):  
Human Factors ◽  
Data Processing ◽  
Electronic Data Processing ◽  
Documentation System ◽  
Electronic Data ◽  
Future Challenges ◽  
History Of ◽  
On Line ◽  
Documentation Systems ◽  
Paper Documentation

The present paper provides a brief history of Electronic Data Processing Systems and their supporting user documentation. The problems inherent to paper documentation are highlighted. Techniques employed to improve on-line reject messages in addition to the development of an online user documentation system are discussed. Future challenges facing the human factors specialist involved in development of on-line documentation are outlined.

Advanced monitoring techniques for a large‐scale data‐processing network

2008 ◽  
Vol 25 (5) ◽  
pp. 287-300 ◽  
Author(s):  
B. Martin ◽  
A. Al‐Shabibi ◽  
S.M. Batraneanu ◽  
Ciobotaru ◽  
G.L. Darlea ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Monitoring Techniques ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Network ◽  
Scale Data

Algorithm Research on Tower Grounding Resistance On-Line Measurement

2012 ◽  
Vol 239-240 ◽  
pp. 372-375
Author(s):  
Tao Wang ◽  
Bing Li ◽  
Hong Xing Yang
Keyword(s):  
Data Processing ◽  
Transmission Line ◽  
Matlab Simulation ◽  
Grounding Resistance ◽  
Line Measurement ◽  
On Line ◽  
Good Conductor

Traditional transmission line tower grounding resistance measurements have many shortcomings, in order to make up the defects the model of transmission line tower with good conductor wires and multiple grounding leads was builded, and then the algorithm of tower grounding resistance on-line measurement was proposed. On this condition, a system based on this algorithm was designed and further analysis of the error during data processing was conducted. At last, the MATLAB simulation indicates that the algorithm has a accuracy that meets the practical requirements.

Sign in / Sign up

Export Citation Format

Share Document