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TB Mobile specifications

Information


Unique identifier OMICS_17248
Name TB Mobile
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Computer skills Medium
Version 2.0.1
Stability Stable
Mobile operating system Android
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Mobile Molecular DataSheet Team

Publication for TB Mobile

TB Mobile citations

 (8)
library_books

Accuracy of an automated system for tuberculosis detection on chest radiographs in high risk screening

2018
PMCID: 5905390
PMID: 29663963
DOI: 10.5588/ijtld.17.0492

[…] pulmonaires (cxr) qui requièrent une interprétation par des experts., evaluer la performance de logiciels de détection numérique pour le tri des cxr au sein d'un programme de dépistage de tb mobile numérique à haut débit., une évaluation rétrospective du logiciel a été réalisée sur une base de données de 38 961 cxr de face réalisées entre 2005 et 2010 chez des patients dont 87 ont eu […]

library_books

Machine learning models identify molecules active against the Ebola virus in vitro

2017
F1000Res
PMCID: 4706063
PMID: 26834994
DOI: 10.5256/f1000research.7773.r10995

[…] and we have developed cheminformatics mobile apps , – . several of these apps combine bayesian models and open source fingerprint descriptors to enable models that can be used within a mobile app (tb mobile, mmds, approved drugs and molprime). this enables a scientist to select a molecule and score it with models. in the current study we used the same training sets for the anti-ebov activity […]

library_books

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)

2016
J Chem Inf Model
PMCID: 4962118
PMID: 27335215
DOI: 10.1021/acs.jcim.6b00004

[…] learning. we also compared the 60 new compounds tested in the in vivo mouse mtb model to the previously described 805 compounds with known mtb targets collated from the literature and available in tb mobile (version 2). this pca model essentially represents the published target-chemistry property space for mtb., we have previously described the generation and validation […]

library_books

Combining Metabolite Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

2015
PLoS One
PMCID: 4627656
PMID: 26517557
DOI: 10.1371/journal.pone.0141076

[…] so other methods must be used for target identification [, ]. for example, we have contributed computational methods that rely on similarity of compounds to inhibitors of known targets [] to create tb mobile 2 which applies a machine learning approach to predict target likelihood., since a small fraction of mtb proteins are known to be modulated by approved tb drugs [], a need exists […]

library_books

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

2015
PLoS Negl Trop Dis
PMCID: 4482694
PMID: 26114876
DOI: 10.1371/journal.pntd.0003878

[…] attempted to predict these in this study. our prior work on mtb resulted in many datasets relating to small molecules and their targets in the bacteria, which in turn lead to the development of the tb mobile app which contains bayesian models that can be used for target prediction [,,]. while we do not have as much published data for t. cruzi a similar approach could be undertaken in future […]

library_books

Mycobacterial Dihydrofolate Reductase Inhibitors Identified Using Chemogenomic Methods and In Vitro Validation

2015
PLoS One
PMCID: 4370846
PMID: 25799414
DOI: 10.1371/journal.pone.0121492

[…] has paved the way to an array of computational target prediction approaches for tb. to date, 139 compounds were predicted to target proteins belonging to diverse biochemical pathways. in addition, tb mobile, [] platforms has been used to predict targets for these phenotypic hits. targets predicted from both methods include essential protein kinases and proteins in the folate pathway, as well […]


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TB Mobile institution(s)
Collaborative Drug Discovery, Burlingame, CA, USA; Collaborations in Chemistry, Fuquay-Varina, NC, USA; Molecular Materials Informatics, Montreal, QC, Canada; SRI International, Menlo Park, CA, USA
TB Mobile funding source(s)
This work was supported by the Bill and Melinda Gates Foundation (Grant#49852) and by Award Number 2R42AI088893-02 from the National Institutes of Allergy and Infectious Diseases.

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