Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis

Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine lea...

Full description

Saved in:
Bibliographic Details
Published inGut microbes Vol. 16; no. 1; p. 2336877
Main Authors Zhu, Jinlin, Yin, Jialin, Chen, Jing, Hu, Mingyi, Lu, Wenwei, Wang, Hongchao, Zhang, Hao, Chen, Wei
Format Journal Article
LanguageEnglish
Published United States Taylor & Francis 31.12.2024
Taylor & Francis Group
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
AbstractList Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
ABSTRACTUlcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of Parabacteroides merdae ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To identify the potential alleviators of UC, we employed an integrative analysis combining microbial community modeling with advanced machine learning techniques. Using metagenomics data sourced from the Integrated Human Microbiome Project, we constructed individualized microbiome community models for each participant. Our analysis highlighted a significant decline in both α and β-diversity of strain-level microbial populations in UC subjects compared to controls. Distinct differences were also observed in the predicted fecal metabolite profiles and strain-to-metabolite contributions between the two groups. Using tree-based machine learning models, we successfully identified specific microbial strains and their associated metabolites as potential alleviators of UC. Notably, our experimental validation using a dextran sulfate sodium-induced UC mouse model demonstrated that the administration of ATCC 43,184 and N-acetyl-D-mannosamine provided notable relief from colitis symptoms. In summary, our study underscores the potential of an integrative approach to identify novel therapeutic avenues for UC, paving the way for future targeted interventions.
Author Lu, Wenwei
Chen, Wei
Yin, Jialin
Wang, Hongchao
Zhang, Hao
Hu, Mingyi
Zhu, Jinlin
Chen, Jing
Author_xml – sequence: 1
  givenname: Jinlin
  surname: Zhu
  fullname: Zhu, Jinlin
  organization: Jiangnan University
– sequence: 2
  givenname: Jialin
  surname: Yin
  fullname: Yin, Jialin
  organization: Jiangnan University
– sequence: 3
  givenname: Jing
  surname: Chen
  fullname: Chen, Jing
  organization: Jiangnan University
– sequence: 4
  givenname: Mingyi
  surname: Hu
  fullname: Hu, Mingyi
  organization: Jiangnan University
– sequence: 5
  givenname: Wenwei
  surname: Lu
  fullname: Lu, Wenwei
  organization: Jiangnan University
– sequence: 6
  givenname: Hongchao
  surname: Wang
  fullname: Wang, Hongchao
  organization: Jiangnan University
– sequence: 7
  givenname: Hao
  surname: Zhang
  fullname: Zhang, Hao
  organization: Wuxi People's Hospital
– sequence: 8
  givenname: Wei
  surname: Chen
  fullname: Chen, Wei
  email: chenwei66@jiangnan.edu.cn
  organization: Jiangnan University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38563656$$D View this record in MEDLINE/PubMed
BookMark eNqFUk1vEzEUXKEiWkp_AmiPXBLs9a53LQ6AKj4iVeICZ-ut_Zy48trFdlLy7_GSNKIcwBdb45l5fp73vDrzwWNVvaRkSclA3lDRCiJ6vmxI0y4bxvjQ90-qixlfEDG0Z6dzz8-rq5RuSVlt2xPOnlXnbOg44x2_qH6ufMZ1hGx3WIMHt0821fc2b-rJqhhGC66egkbnrF8Xhq4nUBvrsXYI0c_g1quww5jqu5DR51kBzuHOQg4FNSHWW6fwWEQFZ7NNL6qnBlzCq-N-WX3_9PHb9ZfFzdfPq-sPNwvVsS4vBNVEKC2w1cZozbghou0Mim5oTAM9M4pQ1uuB9NCA6TkfkQ-EGI4NUDGyy2p18NUBbuVdtBPEvQxg5W8gxLWEmK1yKPlo1EBY0xONbUfaUTGNTHTCkIbQkRWvdwevu-04oVal2QjukenjG283ch12kpZMBBe0OLw-OsTwY4spy8kmVT4XPIZtkowwSkucLS_UV38WO1V5yK4QugOhxJRSRHOiUCLnMZEPYyLnMZHHMSm6t3_plM0lmzC_2br_qt8f1NaXYCe4D9FpmWHvQjQRvLKli39b_AJ7edlT
CitedBy_id crossref_primary_10_1016_j_csbj_2025_03_012
crossref_primary_10_1016_j_bbadis_2024_167618
crossref_primary_10_1016_j_nutres_2025_02_004
crossref_primary_10_1080_19490976_2025_2476570
crossref_primary_10_3389_fmicb_2025_1556827
crossref_primary_10_59717_j_xinn_life_2024_100105
crossref_primary_10_1039_D4FO02344G
Cites_doi 10.1080/19490976.2021.1915673
10.1101/gr.092759.109
10.1093/bioinformatics/btp352
10.1038/s41598-017-10034-5
10.1371/journal.pcbi.1007084
10.1186/1471-2105-11-489
10.1038/s41586-019-1237-9
10.1136/gutjnl-2014-307873
10.1093/bioinformatics/btu170
10.1038/s41540-018-0063-2
10.1016/j.ebiom.2019.03.009
10.1038/s41564-018-0306-4
10.1039/C1AN15605E
10.1053/j.gastro.2019.07.025
10.1053/j.gastro.2020.12.004
10.1038/nri3707
10.1038/s41587-022-01628-0
10.1053/j.gastro.2020.06.038
10.3389/fmicb.2018.01274
10.1038/s41540-021-00178-6
10.1093/ecco-jcc/jjv223
10.1093/bioinformatics/btac082
10.1093/ecco-jcc/jjab029
10.1177/1535370215584901
10.1021/acs.jafc.0c06755
10.1038/s41575-023-00766-3
10.1021/pr2003598
10.1111/jgh.15232
10.1038/nrgastro.2017.110
10.1038/nbt.1614
10.1038/nbt.3703
10.1016/j.ajpath.2018.01.011
10.1038/s41598-020-70583-0
10.1371/journal.pone.0186178
10.1186/s40168-019-0689-3
10.1038/s41467-021-22989-1
10.1093/bioinformatics/btp324
10.1038/s41591-018-0308-z
10.1016/j.coisb.2021.03.001
10.1016/j.compbiomed.2022.106244
10.1093/nar/gky992
10.1002/mnfr.201700144
10.3389/fnut.2021.818902
10.1146/annurev-med-042320-021020
10.3390/ijms17050632
10.1128/mSystems.00209-17
10.1039/D1FO00875G
10.3892/ijmm.2022.5098
10.1016/j.intimp.2022.108711
10.1371/journal.pone.0085345
10.1371/journal.pone.0146162
10.1073/pnas.0704189104
10.1016/j.chom.2015.09.008
10.1093/bioinformatics/bty445
10.3390/jcm10081749
10.3109/00365521.2016.1101245
10.1155/2020/7694734
10.1038/s41572-020-0205-x
10.1016/j.copbio.2014.12.017
10.1016/j.phrs.2012.10.020
10.1038/s41575-019-0258-z
10.1073/pnas.1116053109
10.1128/mSystems.00913-20
10.1046/j.1432-1327.2001.02001.x
10.1016/j.jff.2023.105416
10.1093/nar/gkj102
10.1038/s41598-017-02606-2
10.1073/pnas.0706625104
10.1038/ismej.2017.44
10.1093/bioinformatics/bty941
10.1016/j.csbj.2022.10.026
10.1128/spectrum.01651-22
10.1038/s41596-018-0098-2
10.1016/j.copbio.2018.07.010
10.5009/gnl18438
10.1038/nprot.2009.203
10.1038/s41579-019-0213-6
ContentType Journal Article
Copyright 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. 2024
2024 The Author(s). Published with license by Taylor & Francis Group, LLC. 2024 The Author(s)
Copyright_xml – notice: 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. 2024
– notice: 2024 The Author(s). Published with license by Taylor & Francis Group, LLC. 2024 The Author(s)
DBID 0YH
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
5PM
DOA
DOI 10.1080/19490976.2024.2336877
DatabaseName Taylor & Francis Open Access
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic



MEDLINE
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 4
  dbid: 0YH
  name: Taylor & Francis Open Access
  url: https://www.tandfonline.com
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Biology
DocumentTitleAlternate J. ZHU ET AL
EISSN 1949-0984
ExternalDocumentID oai_doaj_org_article_6bfc803270de4504bc3de3959f0201b3
PMC10989691
38563656
10_1080_19490976_2024_2336877
2336877
Genre Research Article
Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province
– fundername: National Natural Science Foundation of China
  grantid: No. 32372345
– fundername: Fundamental Research Funds for the Central Universities
  grantid: JUSRP622034
– fundername: National Natural Science Foundation of China
  grantid: No. 32021005, No. 31820103010
GroupedDBID ---
00X
0YH
30N
4.4
53G
ABPEM
ACGFS
ACTIO
ADBBV
ADCVX
AEISY
AGYJP
AIJEM
ALMA_UNASSIGNED_HOLDINGS
AOIJS
AQRUH
BABNJ
BAWUL
BLEHA
CCCUG
DKSSO
EBS
EMOBN
F5P
GROUPED_DOAJ
H13
KYCEM
LJTGL
M4Z
MM.
O9-
OK1
RPM
SNACF
SV3
TDBHL
TFL
TFT
TFW
TR2
TTHFI
AAYXX
AIYEW
CITATION
DGEBU
ABCCY
C1A
CGR
CUY
CVF
DIK
ECM
EIF
EJD
HYE
IPNFZ
NPM
OVD
RIG
TEORI
7X8
5PM
ID FETCH-LOGICAL-c535t-91d09cd9e4dffdd36f0945fe9582f2a73fc0137d807a2af766be6800f6e2a19b3
IEDL.DBID DOA
ISSN 1949-0976
1949-0984
IngestDate Wed Aug 27 01:27:59 EDT 2025
Thu Aug 21 18:34:48 EDT 2025
Thu Jul 10 20:38:34 EDT 2025
Thu Apr 03 06:53:54 EDT 2025
Thu Apr 24 22:59:00 EDT 2025
Tue Jul 01 03:30:44 EDT 2025
Wed Dec 25 09:05:32 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords N-acetyl-D-mannosamine
Parabacteroides merdae ATCC 43184
ulcerative colitis
Genome-scale metabolic model
machine learning
inflammatory bowel disease
biomarker selection
Language English
License open-access: http://creativecommons.org/licenses/by-nc/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c535t-91d09cd9e4dffdd36f0945fe9582f2a73fc0137d807a2af766be6800f6e2a19b3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
These authors contributed equally to this work.
OpenAccessLink https://doaj.org/article/6bfc803270de4504bc3de3959f0201b3
PMID 38563656
PQID 3031133646
PQPubID 23479
ParticipantIDs crossref_primary_10_1080_19490976_2024_2336877
proquest_miscellaneous_3031133646
pubmedcentral_primary_oai_pubmedcentral_nih_gov_10989691
crossref_citationtrail_10_1080_19490976_2024_2336877
doaj_primary_oai_doaj_org_article_6bfc803270de4504bc3de3959f0201b3
informaworld_taylorfrancis_310_1080_19490976_2024_2336877
pubmed_primary_38563656
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2024-12-31
PublicationDateYYYYMMDD 2024-12-31
PublicationDate_xml – month: 12
  year: 2024
  text: 2024-12-31
  day: 31
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle Gut microbes
PublicationTitleAlternate Gut Microbes
PublicationYear 2024
Publisher Taylor & Francis
Taylor & Francis Group
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Group
References e_1_3_5_29_1
e_1_3_5_27_1
e_1_3_5_25_1
e_1_3_5_23_1
e_1_3_5_44_1
e_1_3_5_67_1
e_1_3_5_46_1
e_1_3_5_69_1
e_1_3_5_48_1
e_1_3_5_3_1
e_1_3_5_61_1
e_1_3_5_40_1
e_1_3_5_63_1
e_1_3_5_42_1
e_1_3_5_65_1
e_1_3_5_9_1
e_1_3_5_21_1
e_1_3_5_5_1
e_1_3_5_7_1
e_1_3_5_18_1
e_1_3_5_39_1
e_1_3_5_16_1
e_1_3_5_37_1
e_1_3_5_14_1
e_1_3_5_35_1
e_1_3_5_12_1
e_1_3_5_33_1
e_1_3_5_56_1
e_1_3_5_77_1
e_1_3_5_58_1
e_1_3_5_50_1
e_1_3_5_71_1
e_1_3_5_52_1
e_1_3_5_73_1
e_1_3_5_54_1
e_1_3_5_75_1
e_1_3_5_10_1
e_1_3_5_31_1
e_1_3_5_28_1
e_1_3_5_26_1
e_1_3_5_24_1
e_1_3_5_22_1
e_1_3_5_45_1
e_1_3_5_66_1
e_1_3_5_47_1
e_1_3_5_68_1
e_1_3_5_49_1
e_1_3_5_2_1
e_1_3_5_60_1
e_1_3_5_41_1
e_1_3_5_62_1
e_1_3_5_43_1
e_1_3_5_64_1
e_1_3_5_8_1
e_1_3_5_20_1
e_1_3_5_4_1
e_1_3_5_6_1
e_1_3_5_17_1
e_1_3_5_38_1
e_1_3_5_15_1
e_1_3_5_13_1
e_1_3_5_36_1
e_1_3_5_11_1
e_1_3_5_34_1
e_1_3_5_55_1
e_1_3_5_78_1
e_1_3_5_57_1
e_1_3_5_59_1
e_1_3_5_19_1
e_1_3_5_70_1
e_1_3_5_72_1
e_1_3_5_51_1
e_1_3_5_74_1
e_1_3_5_53_1
e_1_3_5_76_1
e_1_3_5_32_1
e_1_3_5_30_1
References_xml – ident: e_1_3_5_27_1
  doi: 10.1080/19490976.2021.1915673
– ident: e_1_3_5_45_1
  doi: 10.1101/gr.092759.109
– ident: e_1_3_5_39_1
  doi: 10.1093/bioinformatics/btp352
– ident: e_1_3_5_8_1
  doi: 10.1038/s41598-017-10034-5
– ident: e_1_3_5_30_1
  doi: 10.1371/journal.pcbi.1007084
– ident: e_1_3_5_41_1
  doi: 10.1186/1471-2105-11-489
– ident: e_1_3_5_35_1
  doi: 10.1038/s41586-019-1237-9
– ident: e_1_3_5_47_1
  doi: 10.1136/gutjnl-2014-307873
– ident: e_1_3_5_36_1
  doi: 10.1093/bioinformatics/btu170
– ident: e_1_3_5_38_1
  doi: 10.1038/s41540-018-0063-2
– ident: e_1_3_5_33_1
  doi: 10.1016/j.ebiom.2019.03.009
– ident: e_1_3_5_62_1
  doi: 10.1038/s41564-018-0306-4
– ident: e_1_3_5_10_1
  doi: 10.1039/C1AN15605E
– ident: e_1_3_5_20_1
  doi: 10.1053/j.gastro.2019.07.025
– ident: e_1_3_5_6_1
  doi: 10.1053/j.gastro.2020.12.004
– ident: e_1_3_5_73_1
  doi: 10.1038/nri3707
– ident: e_1_3_5_78_1
  doi: 10.1038/s41587-022-01628-0
– ident: e_1_3_5_72_1
  doi: 10.1053/j.gastro.2020.06.038
– ident: e_1_3_5_48_1
  doi: 10.3389/fmicb.2018.01274
– ident: e_1_3_5_18_1
  doi: 10.1038/s41540-021-00178-6
– ident: e_1_3_5_44_1
  doi: 10.1093/ecco-jcc/jjv223
– ident: e_1_3_5_40_1
  doi: 10.1093/bioinformatics/btac082
– ident: e_1_3_5_2_1
  doi: 10.1093/ecco-jcc/jjab029
– ident: e_1_3_5_68_1
  doi: 10.1177/1535370215584901
– ident: e_1_3_5_43_1
  doi: 10.1021/acs.jafc.0c06755
– ident: e_1_3_5_67_1
  doi: 10.1038/s41575-023-00766-3
– ident: e_1_3_5_61_1
  doi: 10.1021/pr2003598
– ident: e_1_3_5_46_1
  doi: 10.1111/jgh.15232
– ident: e_1_3_5_3_1
  doi: 10.1038/nrgastro.2017.110
– ident: e_1_3_5_25_1
  doi: 10.1038/nbt.1614
– ident: e_1_3_5_17_1
  doi: 10.1038/nbt.3703
– ident: e_1_3_5_60_1
  doi: 10.1016/j.ajpath.2018.01.011
– ident: e_1_3_5_29_1
  doi: 10.1038/s41598-020-70583-0
– ident: e_1_3_5_50_1
  doi: 10.1371/journal.pone.0186178
– ident: e_1_3_5_19_1
  doi: 10.1186/s40168-019-0689-3
– ident: e_1_3_5_32_1
  doi: 10.1038/s41467-021-22989-1
– ident: e_1_3_5_37_1
  doi: 10.1093/bioinformatics/btp324
– ident: e_1_3_5_54_1
  doi: 10.1038/s41591-018-0308-z
– ident: e_1_3_5_31_1
  doi: 10.1016/j.coisb.2021.03.001
– ident: e_1_3_5_34_1
  doi: 10.1016/j.compbiomed.2022.106244
– ident: e_1_3_5_24_1
  doi: 10.1093/nar/gky992
– ident: e_1_3_5_53_1
  doi: 10.1002/mnfr.201700144
– ident: e_1_3_5_7_1
  doi: 10.3389/fnut.2021.818902
– ident: e_1_3_5_5_1
  doi: 10.1146/annurev-med-042320-021020
– ident: e_1_3_5_12_1
  doi: 10.3390/ijms17050632
– ident: e_1_3_5_51_1
  doi: 10.1128/mSystems.00209-17
– ident: e_1_3_5_52_1
  doi: 10.1039/D1FO00875G
– ident: e_1_3_5_70_1
  doi: 10.3892/ijmm.2022.5098
– ident: e_1_3_5_77_1
  doi: 10.1016/j.intimp.2022.108711
– ident: e_1_3_5_71_1
  doi: 10.1371/journal.pone.0085345
– ident: e_1_3_5_64_1
  doi: 10.1371/journal.pone.0146162
– ident: e_1_3_5_65_1
  doi: 10.1073/pnas.0704189104
– ident: e_1_3_5_58_1
  doi: 10.1016/j.chom.2015.09.008
– ident: e_1_3_5_22_1
  doi: 10.1093/bioinformatics/bty445
– ident: e_1_3_5_63_1
  doi: 10.3390/jcm10081749
– ident: e_1_3_5_74_1
  doi: 10.3109/00365521.2016.1101245
– ident: e_1_3_5_75_1
  doi: 10.1155/2020/7694734
– ident: e_1_3_5_4_1
  doi: 10.1038/s41572-020-0205-x
– ident: e_1_3_5_23_1
  doi: 10.1016/j.copbio.2014.12.017
– ident: e_1_3_5_26_1
  doi: 10.1016/j.phrs.2012.10.020
– ident: e_1_3_5_55_1
  doi: 10.1038/s41575-019-0258-z
– ident: e_1_3_5_14_1
  doi: 10.1073/pnas.1116053109
– ident: e_1_3_5_56_1
  doi: 10.1128/mSystems.00913-20
– ident: e_1_3_5_66_1
  doi: 10.1046/j.1432-1327.2001.02001.x
– ident: e_1_3_5_76_1
  doi: 10.1016/j.jff.2023.105416
– ident: e_1_3_5_13_1
  doi: 10.1093/nar/gkj102
– ident: e_1_3_5_28_1
  doi: 10.1038/s41598-017-02606-2
– ident: e_1_3_5_57_1
  doi: 10.1073/pnas.0706625104
– ident: e_1_3_5_49_1
  doi: 10.1038/ismej.2017.44
– ident: e_1_3_5_21_1
  doi: 10.1093/bioinformatics/bty941
– ident: e_1_3_5_9_1
  doi: 10.1016/j.csbj.2022.10.026
– ident: e_1_3_5_42_1
  doi: 10.1128/spectrum.01651-22
– ident: e_1_3_5_15_1
  doi: 10.1038/s41596-018-0098-2
– ident: e_1_3_5_11_1
  doi: 10.1016/j.copbio.2018.07.010
– ident: e_1_3_5_69_1
  doi: 10.5009/gnl18438
– ident: e_1_3_5_16_1
  doi: 10.1038/nprot.2009.203
– ident: e_1_3_5_59_1
  doi: 10.1038/s41579-019-0213-6
SSID ssj0000447063
Score 2.3931289
Snippet Ulcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut microbiome. To...
ABSTRACTUlcerative colitis (UC) is a challenging form of inflammatory bowel disease, and its etiology is intricately linked to disturbances in the gut...
SourceID doaj
pubmedcentral
proquest
pubmed
crossref
informaworld
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 2336877
SubjectTerms Animals
biomarker selection
Colitis
Colitis, Ulcerative
Gastrointestinal Microbiome
Genome-scale metabolic model
Humans
inflammatory bowel disease
Inflammatory Bowel Diseases
Machine Learning
Mice
N-acetyl-D-mannosamine
Parabacteroides merdae ATCC 43184
Research Paper
ulcerative colitis
SummonAdditionalLinks – databaseName: Taylor & Francis Open Access
  dbid: 0YH
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagCIlLxbvLS0bimuLYjh0fAVEtSHCiEpwiP8tKbbZqshX8e2YcZ9WtQD1w3F1P4s3MeGac8fcR8kZCVLDR8EqERlcyIJE7OFaVvBIQnqCWM3ga-ctXtTyWn783czfhUNoqsYZOE1BEXqvRua0b5o64t1B3GwZhFKo7Lg-5EKrV-ja5w9FawaTZj-V2m4VJqSc-NZSqUGw-x_OvK-1EqAzkfw3G9G_J6PWeyitB6ug-2S_ZJX03mcMDciv2D8ndiW_y9yPy61MBh4AljtoCR0JxK5aerTIiEwhnbhw8pA4jAj3LzZaRFnaJEwpxELs-B3q-HrHTCCSQjuVyhdX7QGH-dHPqY7mJz-11w2NyfPTx24dlVagXKt-IZgR9BWZ8MFGGlEIQKkEZiH1pTcsTt1okj1iFoWXacpu0Ui4qyD2TitzWxoknZK9f9_GAUG10kAkGcOGkUswwZbxhrk3CRs_Ugsj5cXe-4JIjPcZpVxf40llLHWqpK1pakMOt2PkEzHGTwHvU5XYw4mrnL9YXJ11x00655FsmuGYhyoZJ50WIwjQmQVpdO7Eg5qoldGPeVkkTB0onbpjA69lsOvBhfDFj-7jegBysrDUMkvAwnk5mtJ2maBslIOlekHbHwHb-x-4v_epnxgmvmWmNMvWz_5j0c3IPP07Qli_I3nixiS8hDRvdq-xofwBxkihh
  priority: 102
  providerName: Taylor & Francis
Title Integrative analysis with microbial modelling and machine learning uncovers potential alleviators for ulcerative colitis
URI https://www.tandfonline.com/doi/abs/10.1080/19490976.2024.2336877
https://www.ncbi.nlm.nih.gov/pubmed/38563656
https://www.proquest.com/docview/3031133646
https://pubmed.ncbi.nlm.nih.gov/PMC10989691
https://doaj.org/article/6bfc803270de4504bc3de3959f0201b3
Volume 16
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LbxMxELagEhIX1PIM0MpIXLf1rr1-HKFqFZDoiUrlZO36AZHaTdVsUPn3zNhOlFRIuXDJwfEktmfsmdkdfx8hHwV4hS6YpuK-VZXwSOQOG6uKTnJwT5DLGbyN_O1CTi_F16v2aoPqC2vCMjxwXrgT2UenGW8U80G0TPSO-8BNayIEOnWfcD7hRzeSqXQGC6EyjRok6VgTpOTq-o5mJ9iGTZAeNuK44VxqpbYcU8Lvf4Be-q8Y9GEp5YZvOt8nz0pQST_lyRyQR2F4Tp5kmsk_L8j9l4IJAScb7QoKCcUnsPRmloCYQDhR4uDddOjh6U2qsQy0kEr8pOD-sNhzQW_nIxYYgQSysPyeYdK-oDB-urx2ofyJS1V1i5fk8vzs--m0KowLlWt5O4KaPDPOmyB8jN5zGSH7w3K0Vjex6RSPDiEKvWaqa7qopOyDhJAzytB0ten5K7I3zIfwhlBllBcROjS8F1Iyw6RxhvU68i44JidErJbbugJHjqwY17YuqKUrLVnUki1ampDjtdhtxuPYJfAZdbnujHDaqQGMzBYjs7uMbELMpiXYMT1NiZn6xPIdA_iwMhsLWxffx3RDmC9BDg7UGjoJWIzX2YzWw-S6lRxi7QnRWwa2NY_tb4bZrwQPXjOjjTT12_8x83fkKU4mQ1u-J3vj3TIcQhg29kfkMfsxhU_OLo7S7vsLSb8sxQ
linkProvider Directory of Open Access Journals
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagCNELb-jyNBLXLE7s2PERENUW2j21Um9W4kdZ0WarbhYBv54Zx1ntrkA99Jp4Ns7s2PPI-PsIeS_AK9ReFxl3pcqEQyJ3WFhZsJKDe4JcTuNp5KOpnJyIr6fl6dpZGGyrxBw69EARca_GxY3F6KEl7gMk3pqBH4X0rhDjgnNZKXWb3Cm1VMhiwNl0VWdhQqieUA2lMhQbDvL875c2XFRE8t_CMf1XNLrdVLnmpfYfEDu8X9-c8mO87Jqx_bMF_XgzBTwk91MQSz_2VveI3PLtY3K3p7X8_YT8OkgYFLCT0jqhnlCs-NKLWQR-AuFIwYNn4WGEoxexp9PTRGJxRsHdYnPpgl7OO2xoAglkffk5wyLBgoKW6PLc-vQQG7v4Fk_Jyf6X48-TLDE8ZLbkZQdm4Zi2TnvhQnCOywDZJra_lVURilrxYBES0VVM1UUdlJSNlxDiBumLOtcNf0Z22nnr9whVWjkRYEDBGyEl00xqq1lTBV57y-SIiOFPNTbBnyMLx7nJE0rqoFSDSjVJqSMyXold9vgf1wl8QotZDUb47nhhfnVm0m5gZBNsxXihmPOiZKKx3HmuSx0ges8bPiJ63d5MF6s3oadaMfyaCbwbjNPAVoHff-rWz5cgBxt4DoMEKON5b6yrafKqlBxi-xGpNsx44z0277Sz7xGOPGe60lLnL24w6bfk3uT46NAcHky_vSS7eKtH03xFdrqrpX8NkV_XvIlL-y80j0vy
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jj9MwFLZgEIgL-1JWI3FNcWLHjo9s1QxLxYGRuFnxNlTMtNU0RcCv5z3HqaYVaA5zbfxa-_X5Lc7n7xHyUkBUaIOuCu5rVQiPjdxhYxXRSQ7hCWo5jbeRP0_l_qH48K0e0ISrDKvEGjr2RBHJV-PmXvo4IOJeQd2tGYRRqO4qMa44l41Sl8kVieTheIuDTTfHLEwI1fdTQ6kCxYZ7PP_7pq0IlYj8d2hM_5WM7mIqzwSpyU1ih-X12JQf43Vnx-7PDvPjhdZ_i9zIKSx93dvcbXIpzO-Qq31Ty993ya-DzEABfpS2mfOE4nkvPZkl2icQTg148CY8jPD0JCE6A80tLI4oBFuElq7octEhnAkksOfLzxkeEawoKImuj13IP-IShm91jxxO3n99u1_k_g6Fq3ndgVF4pp3XQfgYvecyQq2J4Le6qWLVKh4dEiL6hqm2aqOS0gYJCW6UoWpLbfl9sjdfzMNDQpVWXkQYUHErpGSaSe00s03kbXBMjogY_lPjMvk59uA4NmXmSB2UalCpJit1RMYbsWXP_nGewBs0mM1gJO9OHyxOj0z2BUba6BrGK8V8EDUT1nEfuK51hNy9tHxE9FlzM106u4l9oxXDz5nAi8E2DTgKfPvTzsNiDXLgvksYJEAZD3pb3UyTN7XkkNmPSLNlxVvr2H4yn31PZOQl042Wunx0gUk_J9e-vJuYTwfTj4_JdXzSU2k-IXvd6To8hbSvs8_Sxv4L4QlKlg
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Integrative+analysis+with+microbial+modelling+and+machine+learning+uncovers+potential+alleviators+for+ulcerative+colitis&rft.jtitle=Gut+microbes&rft.au=Zhu%2C+Jinlin&rft.au=Yin%2C+Jialin&rft.au=Chen%2C+Jing&rft.au=Hu%2C+Mingyi&rft.date=2024-12-31&rft.issn=1949-0984&rft.eissn=1949-0984&rft.volume=16&rft.issue=1&rft.spage=2336877&rft_id=info:doi/10.1080%2F19490976.2024.2336877&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1949-0976&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1949-0976&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1949-0976&client=summon