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A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Research Article

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1416-1429 doi: 10.1631/FITEE.2200662

Abstract: To increase the ability of herb prediction for the given symptoms, we introduce TCM-informed observations

Keywords： Traditional Chinese medicine     Herb recommendation     Knowledge graph     Graph attention network    

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Evidence chain-based causality identification in herb-induced liver injury: exemplification of a well-knownliver-restorative herb

Jiabo Wang,Zhijie Ma,Ming Niu,Yun Zhu,Qingsheng Liang,Yanling Zhao,Jingyuan Song,Zhaofang Bai,Yaming Zhang,Ping Zhang,Na Li,Yakun Meng,Qi Li,Lushan Qin,Guangju Teng,Junling Cao,Baosen Li,Shilin Chen,Yonggang Li,Zhengsheng Zou,Honghao Zhou,Xiaohe Xiao

Frontiers of Medicine 2015, Volume 9, Issue 4,   Pages 457-467 doi: 10.1007/s11684-015-0417-8

Abstract:

Herbal medicines have recently been recognized as the second most common cause of drug-induced liver injury (DILI) in the United States. However, reliable methods to identify the DILI causality of some herbs, such as Heshouwu (dried root of Polygonum multiflorum), remain lacking. In this study, a total of 12 307 inpatients with liver dysfunction and 147 literature-reported cases of Heshouwu DILI were screened. A general algorithm indicated that only 22.5% (9/40) and 30.6% (45/147) of all hospitalization and literature case reports, respectively, demonstrate the high probability of DILI causality of Heshouwu. By contrast, 95% (19/20) of all cases prospectively investigated by pharmacognosy, phytochemistry, and metabolomic tests exhibited highly probable causality, including a patient who was previously incorrectly attributed and a case that was excluded from Heshouwu causality by pharmacognostic evidence. Toxin (heavy metals, pesticides, and mycotoxins) contamination was also excluded from Heshouwu DILI causality. The objectivity of these screening methods for Heshouwu DILI diagnosis addresses safety concerns regarding stilbene-containing herbal medicines and dietary supplements.

Keywords： drug-induced liver injury     pharmacognosy     metabolomics     stilbene     Polygonum multiflorum     Chinese herbal medicine    

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Impact of introducing a herb pasture area into a New Zealand sheep and beef hill country farm system:

Cécile DURANTON, Cory MATTHEW

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 1,   Pages 87-97 doi: 10.15302/J-FASE-2018202

Abstract: One recent innovation is the introduction of a herb pasture area, such as plantain.

Keywords： farm system configuration     herb pasture     metabolic energy budgeting     plantain     sheep and beef farming    

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Multistage analysis method for detection of effective herb prescription from clinical data

Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, Baoyan Liu

Frontiers of Medicine 2018, Volume 12, Issue 2,   Pages 206-217 doi: 10.1007/s11684-017-0525-8

Abstract: multistage analysis method that integrates propensity case matching, complex network analysis, and herbset enrichment analysis was proposed to identify effective herb prescriptions for particular diseasesThen, core network extraction and herb set enrichment were combined to detect core effective herb prescriptionsEffectiveness-based mutual information was used to detect strong herb–symptom relationships.Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for

Keywords： effective prescription detection     herb set enrichment analysis     core network extraction     insomnia     personalized    

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AGRONOMIC AND ENVIRONMENTAL BENEFITS OF REINTRODUCING HERB- AND LEGUME-RICH MULTISPECIES LEYS INTO ARABLE

Frontiers of Agricultural Science and Engineering 2022, Volume 9, Issue 2,   Pages 245-271 doi: 10.15302/J-FASE-2021439

Abstract:

Agricultural intensification and the subsequent decline of mixed farming systems has led to an increase in continuous cropping with only a few fallow or break years, undermining global soil health. Arable-ley rotations incorporating temporary pastures (leys) lasting 1–4 years may alleviate soil degradation by building soil fertility and improving soil structure. However, the majority of previous research on arable-ley rotations has utilized either grass or grass-clover leys within ungrazed systems. Multispecies leys, containing a mix of grasses, legumes, and herbs, are rapidly gaining popularity due to their promotion in agri-environment schemes and potential to deliver greater ecosystem services than conventional grass or grass-clover leys. Livestock grazing in arable-ley rotations may increase the economic resilience of these systems, despite limited research of the effects of multispecies leys on ruminant health and greenhouse gas emissions. This review aims to evaluate previous research on multispecies leys, highlighting areas for future research and the potential benefits and disbenefits on soil quality and livestock productivity. The botanical composition of multispecies leys is crucial, as legumes, deep rooted perennial plants (e.g., Onobrychis viciifolia and Cichorium intybus) and herbs (e.g., Plantago lanceolata) can increase soil carbon, improve soil structure, reduce nitrogen fertilizer requirements, and promote the recovery of soil fauna (e.g., earthworms) in degraded arable soils while delivering additional environmental benefits (e.g., biological nitrification inhibition and enteric methane reduction). Multispecies leys have the potential to deliver biologically driven regenerative agriculture, but more long-term research is needed to underpin evidence-based policy and farmer guidance.

Keywords： bioactive forages / integrated crop-livestock systems / nitrogen cycling / plant secondary metabolites / soil carbon / soil quality    

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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainmentIn this paper, the non-IID nature and characteristics of recommendation are discussed, followed by thetheoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendationThis non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research

Keywords： relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Screening for main components associated with the idiosyncratic hepatotoxicity of a tonic herb, Polygonum

Chunyu Li, Ming Niu, Zhaofang Bai, Congen Zhang, Yanling Zhao, Ruiyu Li, Can Tu, Huifang Li, Jing Jing, Yakun Meng, Zhijie Ma, Wuwen Feng, Jinfa Tang, Yun Zhu, Jinjie Li, Xiaoya Shang, Zhengsheng Zou, Xiaohe Xiao, Jiabo Wang

Frontiers of Medicine 2017, Volume 11, Issue 2,   Pages 253-265 doi: 10.1007/s11684-017-0508-9

Abstract:

The main constituents of a typical medicinal herb, Polygonum multiflorum (Heshouwu

Keywords： Polygonum multiflorum     idiosyncratic hepatotoxicity     metabolomics     stilbene     cis-transisomerization    

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Toward Privacy-Preserving Personalized Recommendation Services Review

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Engineering 2018, Volume 4, Issue 1,   Pages 21-28 doi: 10.1016/j.eng.2018.02.005

Abstract:

Recommendation systems are crucially important for the delivery of personalized services to users.With personalized recommendation services, users can enjoy a variety of targeted recommendations suchIn addition, personalized recommendation services have become extremely effective revenue drivers forWe present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services.

Keywords： Privacy protection     Personalized recommendation services     Targeted delivery     Collaborative filtering     Machine    

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Screening for main components associated with the idiosyncratic hepatotoxicity of a tonic herb,

Chunyu Li, Ming Niu, Zhaofang Bai, Congen Zhang, Yanling Zhao, Ruiyu Li, Can Tu, Huifang Li, Jing Jing, Yakun Meng, Zhijie Ma, Wuwen Feng, Jinfa Tang, Yun Zhu, Jinjie Li, Xiaoya Shang, Zhengsheng Zou, Xiaohe Xiao, Jiabo Wang

Frontiers of Medicine 2021, Volume 15, Issue 2,   Pages 330-332 doi: 10.1007/s11684-020-0819-0

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Rare tumors: a blue ocean of investigation

Frontiers of Medicine 2023, Volume 17, Issue 2,   Pages 220-230 doi: 10.1007/s11684-023-0984-z

Abstract: Lastly, we pinpointed the current recommendation chance for patients with rare tumors to be involved

Keywords： rare tumors     diagnosis flowchart     treatment strategy     clinical trials recommendation    

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A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7,   Pages 532-540 doi: 10.1631/FITEE.1400368

Abstract: In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interestA questionnaire survey proved user satisfaction with recommendation results when the cold-start problem

Keywords： Recommender system     Collaborative filtering     Social tagging     Interest evolution model    

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EncyCatalogRec: catalog recommendation for encyclopedia article completion Article

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 436-447 doi: 10.1631/FITEE.1800363

Abstract: So, the recommendation problem is changed to a transductive learning problem in the product graph.Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation

Keywords： Catalog recommendation     Encyclopedia article completion     Product graph     Transductive learning    

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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: Next point-of-interest (POI) recommendation is an important personalized task in location-based social

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Fast code recommendation via approximate sub-tree matching Research Article

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1205-1216 doi: 10.1631/FITEE.2100379

Abstract: Software developers often write code that has similar functionality to existing code segments. A tool that helps developers reuse these code fragments can significantly improve their efficiency. Several methods have been proposed in recent years. Some use sequence matching algorithms to find the related recommendations. Most of these methods are time-consuming and can leverage only low-level textual information from code. Others extract features from code and obtain similarity using numerical feature vectors. However, the similarity of feature vectors is often not equivalent to the original code’s similarity. Structural information is lost during the process of transforming abstract syntax trees into vectors. We propose an approximate sub-tree matching based method to solve this problem. Unlike existing tree-based approaches that match feature vectors, it retains the tree structure of the query code in the matching process to find code fragments that best match the current query. It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list. In this way, the structural information can be used for tasks that have high time requirements. We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method. The results show that our method outperforms two compared methods, SENSORY and Aroma, in terms of the recall value on all the datasets, and can be applied to large datasets.

Keywords： Code reuse     Code recommendation     Tree similarity     Structure information    

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Title Author Date Type Operation

A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation

Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn

Journal Article

Evidence chain-based causality identification in herb-induced liver injury: exemplification of a well-knownliver-restorative herb

Jiabo Wang,Zhijie Ma,Ming Niu,Yun Zhu,Qingsheng Liang,Yanling Zhao,Jingyuan Song,Zhaofang Bai,Yaming Zhang,Ping Zhang,Na Li,Yakun Meng,Qi Li,Lushan Qin,Guangju Teng,Junling Cao,Baosen Li,Shilin Chen,Yonggang Li,Zhengsheng Zou,Honghao Zhou,Xiaohe Xiao

Journal Article

Impact of introducing a herb pasture area into a New Zealand sheep and beef hill country farm system:

Cécile DURANTON, Cory MATTHEW

Journal Article

Multistage analysis method for detection of effective herb prescription from clinical data

Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, Baoyan Liu

Journal Article

AGRONOMIC AND ENVIRONMENTAL BENEFITS OF REINTRODUCING HERB- AND LEGUME-RICH MULTISPECIES LEYS INTO ARABLE

Journal Article

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Screening for main components associated with the idiosyncratic hepatotoxicity of a tonic herb, Polygonum

Chunyu Li, Ming Niu, Zhaofang Bai, Congen Zhang, Yanling Zhao, Ruiyu Li, Can Tu, Huifang Li, Jing Jing, Yakun Meng, Zhijie Ma, Wuwen Feng, Jinfa Tang, Yun Zhu, Jinjie Li, Xiaoya Shang, Zhengsheng Zou, Xiaohe Xiao, Jiabo Wang

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

Screening for main components associated with the idiosyncratic hepatotoxicity of a tonic herb,

Chunyu Li, Ming Niu, Zhaofang Bai, Congen Zhang, Yanling Zhao, Ruiyu Li, Can Tu, Huifang Li, Jing Jing, Yakun Meng, Zhijie Ma, Wuwen Feng, Jinfa Tang, Yun Zhu, Jinjie Li, Xiaoya Shang, Zhengsheng Zou, Xiaohe Xiao, Jiabo Wang

Journal Article

Rare tumors: a blue ocean of investigation

Journal Article

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Journal Article

EncyCatalogRec: catalog recommendation for encyclopedia article completion

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Journal Article

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

Fast code recommendation via approximate sub-tree matching

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Journal Article