Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy o...Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy of the primary tumor which is tightly linked with the expression profile of the molecular features.^(2)Therefore,in this study,we developed a molecular feature-based survival prediction model of OC using a deep neural network(DNN).展开更多
Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks.Unlike approaches using the Nystr?m method,which randomly sampl...Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks.Unlike approaches using the Nystr?m method,which randomly samples the training examples,we make use of random Fourier features,whose basis functions(i.e.,cosine and sine)are sampled from a distribution independent from the training sample set,to cluster preference data which appears extensively in recommender systems.Firstly,we propose a two-stage preference clustering framework.In this framework,we make use of random Fourier features to map the preference matrix into the feature matrix,soon afterwards,utilize the traditional k-means approach to cluster preference data in the transformed feature space.Compared with traditional preference clustering,our method solves the problem of insufficient memory and greatly improves the efficiency of the operation.Experiments on movie data sets containing 100000 ratings,show that the proposed method is more effective in clustering accuracy than the Nystr?m and k-means,while also achieving better performance than these clustering approaches.展开更多
Background:One Health has become a global consensus to deal with complex health problems.However,the pro‑gress of One Health implementation in many countries is still relatively slow,and there is a lack of systematic ...Background:One Health has become a global consensus to deal with complex health problems.However,the pro‑gress of One Health implementation in many countries is still relatively slow,and there is a lack of systematic evalua‑tion index.The purpose of this study was to establish an indicator framework for global One Health Intrinsic Drivers index(GOH-IDI)to evaluate human,animal and environmental health development process globally.Method:First,82 studies were deeply analyzed by a grounded theory(GT)method,including open coding,axial coding,and selective coding,to establish a three-level indicator framework,which was composed of three selective codes,19 axial codes,and 79 open codes.Then,through semi-structured interviews with 28 health-related experts,the indicators were further integrated and simplifed according to the inclusion criteria of the indicators.Finally,the fuzzy analytical hierarchy process combined with the entropy weight method was used to assign weights to the indi‑cators,thus,forming the evaluation indicator framework of human,animal and environmental health development process.Results:An indicator framework for GOH-IDI was formed consisting of three selective codes,15 axial codes and 61 open codes.There were six axial codes for“Human Health”,of which“Infectious Diseases”had the highest weight(19.76%)and“Injuries and Violence”had the lowest weight(11.72%).There were four axial codes for“Animal Health”,of which“Animal Epidemic Disease”had the highest weight(39.28%)and“Animal Nutritional Status”had the low‑est weight(11.59%).Five axial codes were set under“Environmental Health”,among which,“Air Quality and Climate Change”had the highest weight(22.63%)and“Hazardous Chemicals”had the lowest weight(17.82%).Conclusions:An indicator framework for GOH-IDI was established in this study.The framework were universal,balanced,and scientifc,which hopefully to be a tool for evaluation of the joint development of human,animal and environmental health in diferent regions globally.展开更多
Dear Editor,Gastric cancer(GC)is a considerable global health burden;the median survival of advaneed GC is less than 1 year.^(1)Cancer stem cells(CSCs),a small population of cancer cells with stem cell-like properties...Dear Editor,Gastric cancer(GC)is a considerable global health burden;the median survival of advaneed GC is less than 1 year.^(1)Cancer stem cells(CSCs),a small population of cancer cells with stem cell-like properties,are the major cause of treatme nt failure,in eluding GC,2 however,the mechanisms underlying stemness maintenanee of GC stem cells(GCSCs)are still poorly understood.展开更多
基金supported by Chongqing Science&Technol-ogy Bureau(China)(No.CSTB2022NSCQ-MSX1413,cstc2019jscx-msxmX0174,cstc2021ycjh-bgzxm0134).
文摘Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy of the primary tumor which is tightly linked with the expression profile of the molecular features.^(2)Therefore,in this study,we developed a molecular feature-based survival prediction model of OC using a deep neural network(DNN).
基金supported by the National Natural Science Foundation of China(Nos.61872260 and 61592419)the Natural Science Foundation of Shanxi Province(No.201703D421013).
文摘Approximations based on random Fourier features have recently emerged as an efficient and elegant method for designing large-scale machine learning tasks.Unlike approaches using the Nystr?m method,which randomly samples the training examples,we make use of random Fourier features,whose basis functions(i.e.,cosine and sine)are sampled from a distribution independent from the training sample set,to cluster preference data which appears extensively in recommender systems.Firstly,we propose a two-stage preference clustering framework.In this framework,we make use of random Fourier features to map the preference matrix into the feature matrix,soon afterwards,utilize the traditional k-means approach to cluster preference data in the transformed feature space.Compared with traditional preference clustering,our method solves the problem of insufficient memory and greatly improves the efficiency of the operation.Experiments on movie data sets containing 100000 ratings,show that the proposed method is more effective in clustering accuracy than the Nystr?m and k-means,while also achieving better performance than these clustering approaches.
文摘Background:One Health has become a global consensus to deal with complex health problems.However,the pro‑gress of One Health implementation in many countries is still relatively slow,and there is a lack of systematic evalua‑tion index.The purpose of this study was to establish an indicator framework for global One Health Intrinsic Drivers index(GOH-IDI)to evaluate human,animal and environmental health development process globally.Method:First,82 studies were deeply analyzed by a grounded theory(GT)method,including open coding,axial coding,and selective coding,to establish a three-level indicator framework,which was composed of three selective codes,19 axial codes,and 79 open codes.Then,through semi-structured interviews with 28 health-related experts,the indicators were further integrated and simplifed according to the inclusion criteria of the indicators.Finally,the fuzzy analytical hierarchy process combined with the entropy weight method was used to assign weights to the indi‑cators,thus,forming the evaluation indicator framework of human,animal and environmental health development process.Results:An indicator framework for GOH-IDI was formed consisting of three selective codes,15 axial codes and 61 open codes.There were six axial codes for“Human Health”,of which“Infectious Diseases”had the highest weight(19.76%)and“Injuries and Violence”had the lowest weight(11.72%).There were four axial codes for“Animal Health”,of which“Animal Epidemic Disease”had the highest weight(39.28%)and“Animal Nutritional Status”had the low‑est weight(11.59%).Five axial codes were set under“Environmental Health”,among which,“Air Quality and Climate Change”had the highest weight(22.63%)and“Hazardous Chemicals”had the lowest weight(17.82%).Conclusions:An indicator framework for GOH-IDI was established in this study.The framework were universal,balanced,and scientifc,which hopefully to be a tool for evaluation of the joint development of human,animal and environmental health in diferent regions globally.
基金supported by the Science and Technology Commission of Shanghai Municipality Fund(16ZR1420500)the Chongqing Science and Technology Commission(cstc2019jscx-msxmX0174).
文摘Dear Editor,Gastric cancer(GC)is a considerable global health burden;the median survival of advaneed GC is less than 1 year.^(1)Cancer stem cells(CSCs),a small population of cancer cells with stem cell-like properties,are the major cause of treatme nt failure,in eluding GC,2 however,the mechanisms underlying stemness maintenanee of GC stem cells(GCSCs)are still poorly understood.