The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community.The recent ongoing SARSCov2(Severe Acute Respiratory Syndrome)pandemic proved the unpreparedness for ...The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community.The recent ongoing SARSCov2(Severe Acute Respiratory Syndrome)pandemic proved the unpreparedness for these situations.Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently.One major way to find out more information about such pathogens is by extracting the genetic data of such viruses.Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences,there is still limited methods on what new viruses can be generated in future due to mutation.This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM(Recurrent Neural Network-Long ShortTerm Memory)and RNN-GRU(Gated Recurrent Unit)so that in the future,several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus.After the process of testing the model,the F1-recall came out to be more than 0.95.The mutation detection’s accuracy of both the models come out about 98.5%which shows the capability of the recurrent neural network to predict future changes in the genome of virus.展开更多
随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸...随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸多方面的影响。基于循环神经网络(RNN)的方法已经被广泛的应用到下一个兴趣点推荐中,但是这些基于RNN的方法缺乏对用户关系进行深入建模。为了解决上述问题,提出了一种整合用户关系和门控循环单元(GRU)进行下一个兴趣点推荐的模型(GRU-R),同时该模型能够考虑用户签到序列信息、用户关系、兴趣点的时空信息和类别信息等进行下一个兴趣点推荐。在两个真实公开的数据集上进行实验,结果表明提出的模型比现有主流的下一个兴趣点推荐算法具有更高的推荐准确性。展开更多
基金Taif University Researchers are supporting project number(TURSP-2020/211),Taif University,Taif,Saudi Arabia.
文摘The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community.The recent ongoing SARSCov2(Severe Acute Respiratory Syndrome)pandemic proved the unpreparedness for these situations.Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently.One major way to find out more information about such pathogens is by extracting the genetic data of such viruses.Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences,there is still limited methods on what new viruses can be generated in future due to mutation.This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM(Recurrent Neural Network-Long ShortTerm Memory)and RNN-GRU(Gated Recurrent Unit)so that in the future,several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus.After the process of testing the model,the F1-recall came out to be more than 0.95.The mutation detection’s accuracy of both the models come out about 98.5%which shows the capability of the recurrent neural network to predict future changes in the genome of virus.
文摘随着移动设备和社交软件的普遍应用,下一个兴趣点推荐(next POI recommendation)变成了基于位置的社交网络(LBSN)的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸多方面的影响。基于循环神经网络(RNN)的方法已经被广泛的应用到下一个兴趣点推荐中,但是这些基于RNN的方法缺乏对用户关系进行深入建模。为了解决上述问题,提出了一种整合用户关系和门控循环单元(GRU)进行下一个兴趣点推荐的模型(GRU-R),同时该模型能够考虑用户签到序列信息、用户关系、兴趣点的时空信息和类别信息等进行下一个兴趣点推荐。在两个真实公开的数据集上进行实验,结果表明提出的模型比现有主流的下一个兴趣点推荐算法具有更高的推荐准确性。
