Deleted papers

Ref.
PMID		SourceTypeDateTitle#cit
TOT		cit/
year		#cit
INT		TagSel.
 [HELP]  Tag:       
Delete:
Highlight terms:

Use new tag.

Tag name:



Use previously created tags.

 

Ref.
PMID		SourceTypeDateTitle#cit
TOT		cit/
year		#cit
INT		TagSel.
Duvallet, Claire et al.
Nat Commun. (2017).
8(1):1784
29209090		PMS 2017.12Meta-analysis of gut microbiome studies identifies disease-specific and shared responses.35365.210---
LaPierre, Nathan et al.
Methods. (2019).
166:74-82
30885720		PM2019.08MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.266.91---
Oh, Min; Zhang, Liqing
Sci Rep. (2020).
10(1):6026
32265477		PM2020.04DeepMicro: deep representation learning for disease prediction based on microbiome data.268.42---
Ghosh, Tarini S et al.
Elife. (2020).
9
32159510		PM2020.03Adjusting for age improves identification of gut microbiome alterations in multiple diseases.216.60---
Sharma, Divya et al.
Bioinformatics. (2020).
36(17):4544-4550
32449747		PM2020.11TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction.62.40---
Song, Kuncheng et al.
Front Mol Biosci. (2020).
7:610845
33392266		PM2020.00Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction.61.80---
Reitmeier, Sandra et al.
Cell Host Microbe. (2020).
28(2):258-272.e6
32619440		PM2020.08Arrhythmic Gut Microbiome Signatures Predict Risk of Type 2 Diabetes.3111.30---
Sharma, Divya; Xu, Wei
Bioinformatics. (2021).
37(21):3707-3714
34213529		PM2021.11phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data.42.70---
Tierney, Braden T et al.
Nat Commun. (2021).
12(1):2907
34006865		PM2021.05Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators.189.02---
Wirbel, Jakob et al.
Genome Biol. (2021).
22(1):93
33785070		PM2021.03Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.5324.52---
Yang, Fenglong; Zou, Quan
Brief Bioinform. (2021).
22(5)
33834198		PM2021.09DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data.21.20---
Yang, Fenglong et al.
Brief Bioinform. (2021).
22(5)
33515036		PM2021.09GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed.21.20---
Liu, Yang et al.
Cell Metab. (2022).
34(5):719-730.e4
35354069		PM2022.05Early prediction of incident liver disease using conventional risk factors and gut-microbiome-augmented gradient boosting.88.00---
Grazioli, Filippo et al.
PLoS Comput Biol. (2022).
18(4):e1010050
35404958		PM2022.04Microbiome-based disease prediction with multimodal variational information bottlenecks.21.80---
Su, Qi et al.
Nat Commun. (2022).
13(1):6818
36357393		PM2022.11Faecal microbiome-based machine learning for multi-class disease diagnosis.36.00---
Tierney, Braden T et al.
PLoS Biol. (2022).
20(3):e3001556
35235560		PM2022.03Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.54.30---
Giliberti, Renato et al.
PLoS Comput Biol. (2022).
18(4):e1010066
35446845		PMS 2022.04Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.21.80---
Gu, Yian et al.
ISME J. (2022).
16(10):2448-2456
35869387		PM2022.10Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations.46.90---
Lee, Seung Jae; Rho, Mina
Sci Rep. (2022).
12(1):824
35039534		PM2022.01Multimodal deep learning applied to classify healthy and disease states of human microbiome.43.00---
Tierney, Braden T et al.
PLoS Biol. (2022).
20(3):e3001556
35235560		PM2022.03Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.54.30---
Ruuskanen, Matti O et al.
Diabetes Care. (2022).
45(4):811-818
35100347		PM2022.04Gut Microbiome Composition Is Predictive of Incident Type 2 Diabetes in a Population Cohort of 5,572 Finnish Adults.109.20---
Shen, Wan Xiang et al.
Patterns (N Y). (2023).
4(1):100658
36699735		PM2023.01Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.00.00---
D003924Diabetes Mellitus, Type 28/22[+PMIDs]
D001419Bacteriataxid:28/22[+PMIDs]
D005243Feces5/22[+PMIDs]
D015212Inflammatory Bowel Diseases4/22[+PMIDs]
D012336RNA, Ribosomal, 16S3/22[+PMIDs]
D008103Liver Cirrhosis3/22[+PMIDs]
D000328Adult2/22[+PMIDs]
D004194Disease2/22[+PMIDs]
D003922Diabetes Mellitus, Type 12/22[+PMIDs]
D009765Obesity2/22[+PMIDs]
D002318Cardiovascular Diseases2/22[+PMIDs]
D008875Middle Aged2/22[+PMIDs]
D003093Colitis, Ulcerative1/22[+PMIDs]
D008107Liver Diseases1/22[+PMIDs]
D055815Young Adult1/22[+PMIDs]
D000086382COVID-191/22[+PMIDs]
D000068536Firmicutestaxid:12391/22[+PMIDs]
D000368Aged1/22[+PMIDs]
D003424Crohn Disease1/22[+PMIDs]
D005512Food Hypersensitivity1/22[+PMIDs]
D005767Gastrointestinal Diseases1/22[+PMIDs]
D007231Infant, Newborn1/22[+PMIDs]
D000369Aged, 80 and over1/22[+PMIDs]
Details
[+]