about
Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning PerspectiveA multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsCough event classification by pretrained deep neural network.A novel multi-target regression framework for time-series prediction of drug efficacy.Scientific computation of big data in real-world clinical research.Improving prediction accuracy of tumor classification by reusing genes discarded during gene selection.Asymmetric bagging and feature selection for activities prediction of drug moleculesDimension reduction with redundant gene elimination for tumor classification.Selecting subsets of newly extracted features from PCA and PLS in microarray data analysisModelling of inquiry diagnosis for coronary heart disease in Traditional Chinese Medicine by using multi-label learningQualitative and quantitative analysis for facial complexion in traditional Chinese medicine.2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research.Development and psychometric validation of the Chinese version of Skindex-29 and Skindex-16Supervised redundant feature detection for tumor classification.High mean water vapour pressure promotes the transmission of bacillary dysentery.Big data is essential for further development of integrative medicine.Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.Patient classification of hypertension in Traditional Chinese Medicine using multi-label learning techniquesFacial Color Management for Mobile Health in the Wild.MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier.Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion.Analysis and Modeling for Big Data in Cancer Research.Multilabel learning via random label selection for protein subcellular multilocations prediction.Multilabel learning for protein subcellular location prediction.A similarity based learning framework for interim analysis of outcome prediction of acupuncture for neck pain.MAPLSC: a novel multi-class classifier for medical diagnosis.Support vector machine for SAR/QSAR of phenethyl-amines.Predicting membrane protein types with bragging learner.Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins.Degree prediction of malignancy in brain glioma using support vector machines.Dimension reduction for p53 protein recognition by using incremental partial least squares.Semiempirical quantum chemical method and artificial neural networks applied for lambdamax computation of some azo dyes.Irrelevant gene elimination for partial least squares based dimension reduction by using feature probes.Predicting protein structural class with AdaBoost Learner.End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation
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description
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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type
label
Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guozheng LI
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prefLabel
Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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Guo-zheng Li
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P1053
D-5744-2011
P106
P1960
l1D39nsAAAAJ
P2456
l/GuoZhengLi
P31
P3829
P496
0000-0001-5568-0347