典型文献
DrSim:Similarity Learning for Transcriptional Phenotypic Drug Discovery
文献摘要：
Transcriptional phenotypic drug discovery has achieved great success,and various com-pound perturbation-based data resources,such as connectivity map(CMap)and library of inte-grated network-based cellular signatures(LINCS),have been presented.Computational strategies fully mining these resources for phenotypic drug discovery have been proposed.Among them,the fundamental issue is to define the proper similarity between transcriptional profiles.Tra-ditionally,such similarity has been defined in an unsupervised way.However,due to the high dimensionality and the existence of high noise in high-throughput data,similarity defined in the tra-ditional way lacks robustness and has limited performance.To this end,we present DrSim,which is a learning-based framework that automatically infers similarity rather than defining it.We evalu-ated DrSim on publicly available in vitro and in vivo datasets in drug annotation and repositioning.The results indicated that DrSim outperforms the existing methods.In conclusion,by learning tran-scriptional similarity,DrSim facilitates the broad utility of high-throughput transcriptional pertur-bation data for phenotypic drug discovery.The source code and manual of DrSim are available at .
文献关键词：
中图分类号：
作者姓名：
Zhiting Wei;Sheng Zhu;Xiaohan Chen;Chenyu Zhu;Bin Duan;Qi Liu
作者机构：
Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine,Shanghai East Hospital,Bioinformatics Department,School of Life Sciences and Technology,Tongji University,Shanghai 200092,China;Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration(Tongji University),Ministry of Education,Orthopaedic Department of Tongji Hospital,Bioinformatics Department,School of Life Sciences and Technology,Tongji University,Shanghai 200092,China;Shanghai Research Institute for Intelligent Autonomous Systems,Shanghai 201210,China
文献出处：
引用格式：
[1]Zhiting Wei;Sheng Zhu;Xiaohan Chen;Chenyu Zhu;Bin Duan;Qi Liu-.DrSim:Similarity Learning for Transcriptional Phenotypic Drug Discovery)[J].基因组蛋白质组与生物信息学报（英文版）,2022(05):1028-1036
A类：
DrSim,pertur,bation
B类：
Similarity,Learning,Transcriptional,Phenotypic,Drug,Discovery,phenotypic,drug,discovery,has,achieved,great,success,various,com,pound,perturbation,resources,such,connectivity,map,CMap,library,inte,grated,network,cellular,signatures,LINCS,have,been,presented,Computational,strategies,fully,mining,these,proposed,Among,them,fundamental,issue,proper,similarity,between,transcriptional,profiles,ditionally,defined,unsupervised,way,However,due,high,dimensionality,existence,noise,throughput,lacks,robustness,limited,performance,To,this,end,which,learning,framework,that,automatically,infers,rather,than,defining,We,evalu,publicly,available,vitro,vivo,datasets,annotation,repositioning,results,indicated,outperforms,existing,methods,In,conclusion,by,facilitates,broad,utility,code,manual,are
AB值：
0.562661
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