A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gen...A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology.Here,we propose a novel Siamese spectral-based graph convolutional network(SSGCN)model for inferring the protein targets of chemical compounds from gene transcriptional profiles.Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets,and the biological networks under different experiment conditions further complicate the situation,the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles.On a benchmark set and a large time-split validation dataset,the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map.Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound,or reversely,in finding novel inhibitors of a given target of interest.展开更多
为了提高光学遥感卫星信息处理的时效性,文章提出了星地协同光学遥感影像在轨目标智能识别技术框架。将基于遥感大数据的深度学习、神经网络模型训练等数据量大、运算量大、计算复杂、要求较高的处理任务部署在地面服务器,将深度学习训...为了提高光学遥感卫星信息处理的时效性,文章提出了星地协同光学遥感影像在轨目标智能识别技术框架。将基于遥感大数据的深度学习、神经网络模型训练等数据量大、运算量大、计算复杂、要求较高的处理任务部署在地面服务器,将深度学习训练得到的模型进行压缩并上注至卫星,卫星在轨利用轻量化模型对影像进行推理计算,最后把目标识别结果下传至用户。为了对所提出的技术框架进行验证,在高性能服务器和嵌入式开发板上进行了验证试验,利用YOLO-v5算法和DIOR遥感数据集进行了测试,结果表明:在模拟星载计算环境下,处理100km2范围1m分辨率遥感影像耗时17.74s,平均精确度(Mean Average Precision,mAP)为87.2%。文章提出的星地协同智能目标识别技术框架实时性和精度上能够满足应用需求,具备一定的可行性,可以进一步开展在轨验证试验。展开更多
文摘A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology.Here,we propose a novel Siamese spectral-based graph convolutional network(SSGCN)model for inferring the protein targets of chemical compounds from gene transcriptional profiles.Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets,and the biological networks under different experiment conditions further complicate the situation,the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles.On a benchmark set and a large time-split validation dataset,the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map.Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound,or reversely,in finding novel inhibitors of a given target of interest.
文摘为了提高光学遥感卫星信息处理的时效性,文章提出了星地协同光学遥感影像在轨目标智能识别技术框架。将基于遥感大数据的深度学习、神经网络模型训练等数据量大、运算量大、计算复杂、要求较高的处理任务部署在地面服务器,将深度学习训练得到的模型进行压缩并上注至卫星,卫星在轨利用轻量化模型对影像进行推理计算,最后把目标识别结果下传至用户。为了对所提出的技术框架进行验证,在高性能服务器和嵌入式开发板上进行了验证试验,利用YOLO-v5算法和DIOR遥感数据集进行了测试,结果表明:在模拟星载计算环境下,处理100km2范围1m分辨率遥感影像耗时17.74s,平均精确度(Mean Average Precision,mAP)为87.2%。文章提出的星地协同智能目标识别技术框架实时性和精度上能够满足应用需求,具备一定的可行性,可以进一步开展在轨验证试验。
