Automatic disease prediction from human gut metagenomic data using boosting GraphSAGE

[1]  K. Reinert,et al.  A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data , 2023, Bioinform..

[2]  Xiaoping Zhou,et al.  A geometric deep learning framework for drug repositioning over heterogeneous information networks , 2022, bioRxiv.

[3]  Zhuhong You,et al.  A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning , 2021, Cancers.

[4]  Wei Xu,et al.  TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction , 2020, Bioinform..

[5]  Derek Reiman,et al.  PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data , 2020, IEEE Journal of Biomedical and Health Informatics.

[6]  Thanh Hai Nguyen,et al.  Disease Prediction Using Metagenomic Data Visualizations Based on Manifold Learning and Convolutional Neural Network , 2019, FDSE.

[7]  Qing Zhu,et al.  Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification , 2019, Front. Genet..

[8]  Wei Wang,et al.  MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction. , 2019, Methods.

[9]  R. Marculescu,et al.  MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks , 2018, BMC Bioinformatics.

[10]  Yunlong Liu,et al.  Ensemble Learning for Overall Power Conversion Efficiency of the All-Organic Dye-Sensitized Solar Cells , 2018, IEEE Access.

[11]  Nataliya Sokolovska,et al.  Disease Classification in Metagenomics with 2D Embeddings and Deep Learning , 2018, ArXiv.

[12]  Feng Liu,et al.  Deep Learning and Its Applications in Biomedicine , 2018, Genom. Proteom. Bioinform..

[13]  Amnon Amir,et al.  Gut Microbiota Offers Universal Biomarkers across Ethnicity in Inflammatory Bowel Disease Diagnosis and Infliximab Response Prediction , 2018, mSystems.

[14]  Alice C. McHardy,et al.  MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples , 2018, Bioinformatics.

[15]  Cesare Furlanello,et al.  Phylogenetic convolutional neural networks in metagenomics , 2017, BMC Bioinformatics.

[16]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[17]  Aiping Wu,et al.  MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets , 2016, Biophysics reports.

[18]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[19]  T. Martin McGinnity,et al.  A metagenomic hybrid classifier for paediatric inflammatory bowel disease , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[20]  Edoardo Pasolli,et al.  Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights , 2016, PLoS Comput. Biol..

[21]  P. Schloss,et al.  Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions , 2016, Genome Medicine.

[22]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[23]  Duy Tin Truong,et al.  MetaPhlAn2 for enhanced metagenomic taxonomic profiling , 2015, Nature Methods.

[24]  Jens Roat Kultima,et al.  Potential of fecal microbiota for early‐stage detection of colorectal cancer , 2014 .

[25]  Se Jin Song,et al.  The treatment-naive microbiome in new-onset Crohn's disease. , 2014, Cell host & microbe.

[26]  Jan Verhaegen,et al.  A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis , 2013, Gut.

[27]  Miguel Ángel Guevara-López,et al.  Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis , 2012, Journal of Medical Systems.

[28]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[29]  Rob Knight,et al.  Using QIIME to Analyze 16S rRNA Gene Sequences from Microbial Communities , 2011, Current protocols in bioinformatics.

[30]  A. Darzi,et al.  Gut microbiome-host interactions in health and disease , 2011, Genome Medicine.

[31]  M. Robinson,et al.  Small-sample estimation of negative binomial dispersion, with applications to SAGE data. , 2007, Biostatistics.

[32]  J. Handelsman Metagenomics: Application of Genomics to Uncultured Microorganisms , 2004, Microbiology and Molecular Biology Reviews.

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[34]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[35]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[36]  Pengwei Hu,et al.  Fusing Higher and Lower-order Biological Information for Drug Repositioning via Graph Representation Learning , 2023, IEEE Transactions on Emerging Topics in Computing.

[37]  Robert E. Schapire,et al.  Explaining AdaBoost , 2013, Empirical Inference.

[38]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.