Shaanxi University of Science and Technology

International Business Machines Corporation

Xi'an Taikomed Pharmaceutical Technology Co., Ltd.

Shaanxi Panlong Pharmaceutical Co., Ltd.

Shaanxi Panlong Pharmaceutical Group Limited by Share Ltd.

Xixian New District Fenghou Original Pharmaceutical Technology Co., Ltd.

Lei Tian

Chengyuan Liang

Han Li

Xingke Ju

Yuzhi Liu

Qianqian Zhao

Bin Tian

Jingyi Li

Liang Xin

Xiaolin Xie

Dezhu Zhang

Taotao Qiang

Yi Shao

Juan Xia

Xuechuan Wang

Yonghong Tang

Minyi Jia

Nan Qin

Shaojun Zhang

Dan Yang

Juan Li

Nan Hui

Wenbo Yao

Huafeng Qi

Xuhua Zhou

Zhao Ma

Kangxiong Wu

Gennian Mao

Danni Tian

Minghui Chang

Shunjun Ding

Xiuding Yang

Peng Xue

Zhenfeng Shi

Jing James Xu

Ning Zhang

Chun Lei Xu

Yongbo Wang

Liang Qi

Guaiping Qiao

Jingwen Xu

Niall Fraser McCarroll

Limei Wang

Yuanyuan He

Jie Li

Peiyu Zhou

Yuting Liu

Songsong Ruan

Mi Wu

Xuechuang Wang

Xinke Ju

Qingbo Zhao

Min Li

Catherine J Lee

Maggie Lewis

Sundian Liu

Julian J Clinton

Longfang Ren

Shaomeng Pei

Lu Chen

Peng Hui Jiang

Dong Hai Yu

Liang Wang

Wei Wu

Xue Ying Zhang

Si Er Han

Jia Wang

Na Zhao

Jun Wang

Di Ling Chen

Xing Wei

Jie Zhang

Yao Dong Liu

Yanjun Li

Jing Xu

Qiao Zeng

Jiang Bo Kang

Li Na Wang

Yuqing Zhao

Bo Song

Xinhua Liu

Ouyang Yue

Ning Liu

Xuefeng Chen

Kaiqi Shi

Zhe Zl Liu

Zhuanmei Yuan

Lili Wen

Yunying Song

Yuecheng Lyu

Boyang Li

Rong Hong Wan

Xiao Li Zhang

Shi Bin Liu

Hao Zheng

Han Zhang

Runtong Zhu

Liuyi Yang

A computer-implemented system, platform, programing product, and/or method for improving transformation selection in an ensemble machine learning (ML) model that includes: providing all base ML models of the ensemble ML model; identifying all of a plurality of Derived Fields in all the base ML models; performing a Derived Field run prediction analysis for all the Derived Fields; computing the Derived Field Importance Weight for Field (DFIW4F) and the Derived Field Importance Weight for Model (DFIW4M) for all the Derived Fields; clustering all the Derived Fields into a plurality of Derived Field clusters, wherein each Derived Field cluster is based upon the DFIW4M and the DFIW4F for the Derived Field; sorting all the Derived Field clusters by best cluster based upon DFIW4M and DFIW4F; and running the base ML models based upon the Derived Fields in the best Derived Field cluster until sufficient base ML models have been run.

System and method for efficient transformation prediction in a data analytics prediction model pipeline

Determining a class-specific predictor indicator importance can include generating, using machine learning, a prediction model having a tree structure including nodes, class label subgroups, and class-specific predictor indicators. A class-specific predictor indicator importance can be generated for a selected class-specific predictor indicator by merging a term predictor indicator frequency and a purity predictor indicator frequency at each instance of the selected class-specific predictor indicator within one or more nodes of the tree structure. A representation of the tree structure displayed in a graphical user interface can depict each path between a root node of the tree structure and a leaf node mapped to the selected class label subgroup having a node containing an instance of the selected class-specific predictor indicator in a manner that contrasts with other paths between the root node and other leaf nodes of the tree structure.

Class-specific predictor importance

A method, system, and computer for balance weighted voting. The method may comprise receiving, by a network interface, a scoring request. The method may further comprise, by a processing unit in response to the scoring request, generating a plurality of scores using a plurality of models. normalizing the plurality of scores, calculating an evaluation-based weighting factor from a first subset of the normalized scores, calculating a prediction-based based weighting factor from a second subset of the normalized scores, and calculating a balanced weighting predictor from the evaluation-based weighting factor and the prediction-based weighting factor. The method may further comprise returning, by the network interface, the balanced weighting predictor as an ensemble score for the scoring request.

Balance weighted voting

The present invention relates to the technical field of medicinal chemistry, and particularly to a method for preparing a 3-tetrazolylmethyl-1,3,5-triazin-2,4-dione compound inhibiting coronavirus 3CL protease activity and use thereof. Specifically, a compound of Formula I, or a pharmaceutically acceptable salt, or an optical isomer, or an isotope-substituted form thereof is provided. The compound effectively inhibits the SARS-CoV-2 3CLactivity, and is useful in the preparation of a SARS-CoV-2 3CLinhibitor to block the replication and transcription of SARS-CoV-2 viruses in patients. The compound prepared in the present invention has high in-vitro safety, and very good prospect of application in the preparation of SARS-CoV-2 3CLinhibitors and anti-SARS-CoV-2 drugs.

3-tetrazolylmethyl-1,3,5-triazin-2,4-dione compound inhibiting coronavirus 3CL protease activity and preparation method and use thereof

Disclosed are a computer-implemented method, a system and a computer program product for feature processing. In the computer-implemented method for feature processing, two input features selected from multiple features of each sample in a sample set are projected to one resulting feature by one or more processing units based on a specified curve. The sample set is updated by replacing the two input features with the one resulting feature for each sample in the sample set by one or more processing units. The projecting and the updating for the sample set are repeated by one or more processing units until the number of features of each sample in the sample set reaches a predetermined criterion.

Feature processing for machine learning

A pleuromutilin cinnamic acid ester compound with activities against antibiotic-resistant infections having the following formula (I) R, R, R, Rand Rare independently hydrogen, halogen, nitro, cyano, methoxy, trifluoromethyl, benzyloxy, aryl or dimethylamino.

Pleuromutilin cinnamic acid ester compounds with activities against antibiotic-resistant infections

Disclosed is a compound of formula I, a pharmaceutically acceptable salt, or a tautomer thereof. formula I. R, R, Rand Rare independently selected from the group consisting of hydrogen, methyl group, tert-butyl group, methoxy group, difluoromethyl, trifluoromethyl, trifluoromethoxy, nitro, halogen, phenyl and aromatic heterocyclic; Ris a hydrogen or halogen; and Ris hydrogen, C1-4 alkane or C1-4 cycloalkane.

3-triazolyl methyl-1,3,5-triazine-2,4-dione compounds and preparation method and application thereof

A method of applying a hierarchical porous ZrOincludes: dissolving a triblock copolymer in an organic solvent to obtain a solution A, dissolving a tannin extract in distilled water to obtain a solution B, mixing the solution A and the solution B and stirring to obtain a mixed solution; adding a zirconium salt to the mixed solution and stirring; heating the mixed solution in an oven to obtain ZrO, promoting the conversion of the crystal form of ZrO; calcining ZrOat a high temperature to obtain the hierarchical porous ZrO; mixing the hierarchical porous ZrOwith a solution containing a lysozyme protein; and absorbing the lysozyme protein to the hierarchical porous ZrO.

Method of applying hierarchical porous ZrOmaterial

A compound of formula I or formula II, a pharmaceutically acceptable salt, or a tautomer thereof is disclosed. The Linker is or and n is 1-6.

Protacs targeting coronavirus 3CL protease and preparation method and application thereof

A compound of formula I or formula II, a pharmaceutically acceptable salt, or a tautomer thereof is disclosed. In formula I and II, n is 1-6.

Protacs based on VHL ligand targeting coronavirus 3CL protease and preparation method and application thereof

A compound having the formula (I): is disclosed. A method of preparing the compound of formula (I) is also disclosed.

Silibinin lipoic acid ester with hepatoprotective activity and a method of preparing the same

A method for preparing a hierarchical porous ZrOincludes the following steps: S, dissolving a triblock copolymer in an organic solvent to obtain a solution A, dissolving a tannin extract in distilled water to obtain a solution B, mixing the solution A and the solution B and stirring to obtain a mixed solution; S, adding a zirconium salt to the mixed solution obtained in step Sand stirring; S, heating the mixed solution obtained in step Sin an oven to obtain ZrO, promoting the conversion of the crystal form of ZrO; S, calcining ZrOat a high temperature to obtain the hierarchical porous ZrO.

Hierarchical porous ZrOmaterial, method of preparing the same, and application thereof

According to embodiments of the present disclosure, a method, a device and a computer program product for code deployment are proposed. In the method, a deployment strategy for deploying code into a plurality of computing environments and respective amounts of resources provided by the plurality of computing environments are obtained. At least one code segment of the code to be deployed in a corresponding computing environment comprised in the plurality of computing environments is determined based on the deployment strategy and the respective amounts of resources. An amount of resources provided by the corresponding computing environment is sufficient to run the at least one code segment. The at least one code segment is deployed into the corresponding computing environment.

Code deployment

A compound with antibacterial activity having the following formula (I): is disclosed. A method of preparing the compound of formula (I) is also disclosed.

Pleuromutilin ursodeoxycholic acid ester with antibacterial activity and a method of preparing the same

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a request for a predicted ensemble score in real-time. A subset of base model instances is formed by use of a preconfigured priority policy. A fitness score of the formed subset, quantifying the accuracy of the subset, is calculated as a sum of weights respective to the base model instances in the subset. A number of base models represented in the subset is less than or equal to a number of all based models.

Real time ensemble scoring optimization

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Xi'an, China

Lei Tian