To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and app...To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.展开更多
Identifying essential proteins from protein-protein interaction networks is important for studies onbiological evolution and new drug’s development.Most of the presented criteria for prioritizing essential proteinson...Identifying essential proteins from protein-protein interaction networks is important for studies onbiological evolution and new drug’s development.Most of the presented criteria for prioritizing essential proteinsonly focus on a certain attribute of the proteins in the network,which suffer from information loss.In order toovercome this problem,a relatively comprehensive and effective novel method for essential proteins identificationbased on improved multicriteria decision making(MCDM),called essential proteins identification-technique fororder preference by similarity to ideal solution(EPI-TOPSIS),is proposed.First,considering different attributes ofproteins,we propose three methods from different aspects to evaluate the significance of the proteins:gene-degreecentrality(GDC)for gene expression sequence;subcellular-neighbor-degree centrality(SNDC)and subcellular-indegree centrality(SIDC)for subcellular location information and protein complexes.Then,betweenness centrality(BC)and these three methods are considered together as the multiple criteria of the decision-making model.Analytic hierarchy process is used to evaluate the weights of each criterion,and the essential proteins are prioritizedby an ideal solution of MCDM,i.e.,TOPSIS.Experiments are conducted on YDIP,YMIPS,Krogan and BioGRIDnetworks.The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifyingthe essential proteins through the performance measures.展开更多
目的系统评价以团队为基础的教学(TBL)模式和传统以讲授为主的教学(LBL)模式在医学影像学的教学效果。方法计算机检索PubMed、EMbase、Web of Science、WanFang Data、CNKI和VIP数据库,搜集比较以团队为基础的教学法与传统教学法在医学...目的系统评价以团队为基础的教学(TBL)模式和传统以讲授为主的教学(LBL)模式在医学影像学的教学效果。方法计算机检索PubMed、EMbase、Web of Science、WanFang Data、CNKI和VIP数据库,搜集比较以团队为基础的教学法与传统教学法在医学影像学中授课效果的随机对照试验(RCT),检索时限均为建库至2020年3月31日。由2名研究者独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用Stata/SE 16.0软件进行Meta分析。结果共纳入11个RCT,包括721例研究对象。Meta分析结果显示:TBL模式教学组理论考核成绩[SMD=1.70,95%CI(1.05,2.36),P<0.001]、实践考核成绩[SMD=2.00,95%CI(1.02,2.98),P<0.001]、学生对课程满意程度[RR=1.53,95%CI(1.19,1.97),P=0.001],以及团队合作能力[RR=2.46,95%CI(1.69,3.59),P<0.001]、自学思考能力[RR=2.41,95%CI(1.33,4.39),P=0.004]和临床实践能力[RR=2.09,95%CI(1.46,3.00),P<0.001]的提升程度均优于LBL组,但两组在理论知识掌握的主观评价上差异无统计学意义。结论当前证据显示,以主动学习和团队协作为理念的TBL教学模式在医学影像学教学中应用效果相较于传统LBL教学模式有明显的优势。受纳入研究数量和质量的限制,上述结论尚待更多高质量研究予以验证。展开更多
基金Supported by the National Natural Science Foundation of China(U1663208,51520105005)the National Science and Technology Major Project of China(2017ZX05009-005,2016ZX05037-003)
文摘To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.
基金the National Natural Science Foundation of China(Nos.62162040 and 11861045)。
文摘Identifying essential proteins from protein-protein interaction networks is important for studies onbiological evolution and new drug’s development.Most of the presented criteria for prioritizing essential proteinsonly focus on a certain attribute of the proteins in the network,which suffer from information loss.In order toovercome this problem,a relatively comprehensive and effective novel method for essential proteins identificationbased on improved multicriteria decision making(MCDM),called essential proteins identification-technique fororder preference by similarity to ideal solution(EPI-TOPSIS),is proposed.First,considering different attributes ofproteins,we propose three methods from different aspects to evaluate the significance of the proteins:gene-degreecentrality(GDC)for gene expression sequence;subcellular-neighbor-degree centrality(SNDC)and subcellular-indegree centrality(SIDC)for subcellular location information and protein complexes.Then,betweenness centrality(BC)and these three methods are considered together as the multiple criteria of the decision-making model.Analytic hierarchy process is used to evaluate the weights of each criterion,and the essential proteins are prioritizedby an ideal solution of MCDM,i.e.,TOPSIS.Experiments are conducted on YDIP,YMIPS,Krogan and BioGRIDnetworks.The results indicate that EPI-TOPSIS outperforms several state-of-the-art approaches for identifyingthe essential proteins through the performance measures.
文摘目的系统评价以团队为基础的教学(TBL)模式和传统以讲授为主的教学(LBL)模式在医学影像学的教学效果。方法计算机检索PubMed、EMbase、Web of Science、WanFang Data、CNKI和VIP数据库,搜集比较以团队为基础的教学法与传统教学法在医学影像学中授课效果的随机对照试验(RCT),检索时限均为建库至2020年3月31日。由2名研究者独立筛选文献、提取资料并评价纳入研究的偏倚风险后,采用Stata/SE 16.0软件进行Meta分析。结果共纳入11个RCT,包括721例研究对象。Meta分析结果显示:TBL模式教学组理论考核成绩[SMD=1.70,95%CI(1.05,2.36),P<0.001]、实践考核成绩[SMD=2.00,95%CI(1.02,2.98),P<0.001]、学生对课程满意程度[RR=1.53,95%CI(1.19,1.97),P=0.001],以及团队合作能力[RR=2.46,95%CI(1.69,3.59),P<0.001]、自学思考能力[RR=2.41,95%CI(1.33,4.39),P=0.004]和临床实践能力[RR=2.09,95%CI(1.46,3.00),P<0.001]的提升程度均优于LBL组,但两组在理论知识掌握的主观评价上差异无统计学意义。结论当前证据显示,以主动学习和团队协作为理念的TBL教学模式在医学影像学教学中应用效果相较于传统LBL教学模式有明显的优势。受纳入研究数量和质量的限制,上述结论尚待更多高质量研究予以验证。
