Search

Your search keyword '"Zheng, Huiru"' showing total 131 results
Start Over Author "Zheng, Huiru" Publisher ieee
131 results on '"Zheng, Huiru"'

9. Towards Continuous Professional Monitoring of Health Status Based on Energetic Balancing

Author

Vu, Binh, primary, Bruchhaus, Sebastian, additional, Moorhead, Anne, additional, Zheng, Huiru, additional, D'Arco, Luigi, additional, Lynch, Louise, additional, Sica, Luigia Simona, additional, Ponticorvo, Michela, additional, Diano, Federico, additional, Afli, Haithem, additional, Joshi, Praveen, additional, Molinari, Andrea, additional, and Hemmje, Matthias, additional

Published
2022
Full Text
View/download PDF

11. Mining Similar Aspects for Gene Similarity Explanation Based on Gene Information Network.

Author

Zhang, Yidan, Duan, Lei, Zheng, Huiru, Li-Ling, Jesse, Qin, Ruiqi, Chen, Zihao, He, Chengxin, and Wang, Tingting

Abstract

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

16. On accurate, automated and insightful deviation analysis of clinical protocols

Author

Yan, Hui, Lu, Xudong, van Gorp, Pieter, Heines, Serge J.H., Nan, Shan, van Mook, Walther, Bergmans, Dennis, Kaymak, Uzay, Duan, Huilong, Schmidt, Harald, Griol, David, Wang, Haiying, Baumbach, Jan, Zheng, Huiru, Callejas, Zoraida, Hu, Xiaohua, Dickerson, Julie, Zhang, Le, Information Systems IE&IS, and Signal Processing Systems

Subjects
pathway, business.industry, Computer science, Best practice, deviation, 02 engineering and technology, mining, redesign, Intensive care unit, Automation, law.invention, Software, Work (electrical), law, 020204 information systems, Compliance analysis, 0202 electrical engineering, electronic engineering, information engineering, Clinical protocol, 020201 artificial intelligence & image processing, Software engineering, business
Abstract

Clinical guidelines, pathways and protocols are introduced to standardize and provide best-practice care. Analyzing deviations of actual care against the documented best practices is useful to find opportunities for complying better in the future. Prior work demonstrates that deviation analyses should be accurate, automated and insightful but only few studies manage to satisfy all three intentions. In this paper, we manage to reconcile accuracy with automation and insightfulness by combining the previously disconnected steps of checking and mining in compliance analysis software. Results are achieved using an algorithm that consists of three steps. We demonstrate the effectiveness of the algorithm via a real-life case from the intensive care unit of a Dutch hospital.

Published
2018
Full Text
View/download PDF

19. Automated Object Tracking for Animal Behaviour Studies

Author

Manning, Timmy, primary, Somarriba, Miguel, additional, Roehe, Rainer, additional, Turner, Simon, additional, Wang, Haiying, additional, Zheng, Huiru, additional, Kelly, Brian, additional, Lynch, Jennifer, additional, and Walsh, Paul, additional

Published
2019
Full Text
View/download PDF

23. An Ontology-Independent Representation Learning for Similar Disease Detection Based on Multi-Layer Similarity Network.

Author

Qin, Ruiqi, Duan, Lei, Zheng, Huiru, Li-Ling, Jesse, Song, Kaiwen, and Zhang, Yidan

Abstract

To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently, most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then, a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

24. A Knowledge-Driven Network-Based Analytical Framework for the Identification of Rumen Metabolites.

Author

Wang, Mengyuan, Wang, Haiying, Zheng, Huiru, Dewhurst, Richard, and Roehe, Rainer

Abstract

Metabolites are the final production of biochemical reactions in the rumen micro-ecological system and are very sensitive to changes in rumen microbes. Nuclear magnetic resonance (NMR) spectroscopy could both identify and quantify the metabolic composition of the ruminal fluid, which reflects the interaction between rumen microbes and diet. The main challenge of untargeted metabolomics is the compound annotation. Based on non-linear and linear associations between microbial gene abundances and integrals derived from NMR spectra, combined with knowledge of enzymatic reaction from the KEGG database, this study developed a knowledge-driven network-based analytical framework for the inference of metabolites. There were 89 potential metabolites inferred from the integral co-occurrence network. The results are supported by dissimilarity network analysis. The coexistence of non-linear and linear associations between microbial gene abundances and spectral integrals was detected. The study successfully found the corresponding integrals for acetate, butyrate and propionate, which are the major volatile fatty acids (VFA) in the rumen. This novel framework could very efficiently infer metabolites to corresponding integrals from NMR spectra. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

31. Multiscale Computing in Systems Medicine: a Brief Reflection

Author

Zheng, Huiru, primary, Wassan, Jyotsna Talreja, additional, Moisescu, Mihnea Alexandru, additional, Stoicu-Tivadar, Lacramioara, additional, Miranda, Joao, additional, Crisan-Vida, Mihaela, additional, Sacala, Ioan Stefan, additional, Badnjevic, Almir, additional, Chorbev, Ivan, additional, and Jakimovski, Boro, additional

Published
2018
Full Text
View/download PDF

46. Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance.

Author

Wassan, Jyotsna Talreja, Wang, Haiying, Browne, Fiona, and Zheng, Huiru

Abstract

High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomic functions. Machine learning (ML) models can be trained using microbial features which are then used to classify microbial data into different functional classes. For example, ML analyses over the human microbiome data has been linked to the prediction of important biological states. For analysing omics data, integrating abundance count of taxonomical features with their biological relationships is important. These relationships can potentially be uncovered from the phylogenetic tree of microbial taxa. In this paper, we propose a novel integrative framework Phy-PMRFI. This framework is driven by the phylogeny-based modeling of omics data to predict metagenomic functions using important features selected by a random forest importance (RFI) strategy. The proposed framework integrates the underlying phylogenetic tree information with abundance measures of microbial species (features) by creating a novel phylogeny and abundance aware matrix structure (PAAM). Phy-PMRFI progresses by ranking the microbial features using an RFI measure. This is then used as input for microbiome classification. The resultant feature set enhances the performance of the state-of-art methods such as support vector machines. Our proposed integrative framework also outperforms the state-of-the-art pipeline of phylogenetic isometric log-ratio transform (PhILR) and MetaPhyl. Prediction accuracy of 90 % is obtained with Phy-PMRFI over human throat microbiome in comparison to other approaches of PhILR with 53% and MetaPhyl with 71% accuracy. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

47. A Comprehensive Study on Predicting Functional Role of Metagenomes Using Machine Learning Methods.

Author

Wassan, Jyotsna Talreja, Wang, Haiying, Browne, Fiona, and Zheng, Huiru

Abstract

"Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the "curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN). [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results