Design And Implementation Of Biological Pathway Search And Visiualization System

Biological pathways contain rich biological knowledge such as biochemical reaction mechanism,disease pathogenic mechanism and biological macromolecule characteristics.With the continuous development of experimental techniques and methods in the field of biology,more and more biological pathways have been discovered.Therefore,how to use computer technology to quickly and accurately search and visualize biological pathways has become an urgent scientific research problem.Although there are already some software systems that can search and visualize pathways,there are still some problems with these existing search and visualization solutions.On the one hand,the search mechanism of these systems is not smart enough to provide different ways of sorting the pathway list for different users,nor to reorganize the pathway list as the users' preferences change dynamically.On the other hand,when these systems visualize biological pathways,they usually only show the pathway in the form of pictures,and the interactive functions supported are very limited.In order to solve the above problems,this paper proposes an intelligent search method for biological pathways,and on this basis,a pathway search and visualization software system is developed.This subject has designed a learning to rank algorithm based on multi-domain features to complete the intelligent search task of biological pathways.First,by simulating the users' behavior of searching for biological pathways,a simulation dataset is constructed,and feature extraction is performed from the three domains of natural language,biological information,and historical behavior similarity during the simulation process,so that the simulation dataset contains more abundant feature information.In order to complete the online training of the model,the cascading click model is used to simulate the users' click and browse operations on the result list,and on this basis,the model is optimized online using pairwise differentiable gradient descent(PDGD),so that the model has the ability to continuously learn and update itself from the users' search behavior.The final experimental results prove that the learning to rank algorithm based on multi-domain features designed in this subject not only ensures that the model can return a high-quality pathway list for users,but also makes the model have better performance in terms of convergence speed and stability.Finally,on the basis of the above work,we design and implement a pathway search and visualization system,which consists of a search subsystem and a visualization subsystem.By constantly learning from the users' behavior,the search subsystem can provide users with a more intelligent pathway search service.The visualization subsystem visualizes pathways in the form of dynamic graphs and provides rich interactive functions,including style editing,layout adjustment,information acquisition,and data annotation.The system can greatly facilitate the process of searching and analyzing biological pathways.