Literature DB >> 33396562

System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation.

Nicolas Borisov1, Yaroslav Ilnytskyy2,3, Boseon Byeon2,3,4, Olga Kovalchuk2,3, Igor Kovalchuk2,3.   

Abstract

There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).

Entities:  

Keywords:  anti-inflammatory properties; cannabis drug efficiency index; signaling pathway impact analysis

Mesh:

Substances:

Year:  2020        PMID: 33396562      PMCID: PMC7795809          DOI: 10.3390/ijms22010388

Source DB:  PubMed          Journal:  Int J Mol Sci        ISSN: 1422-0067            Impact factor:   5.923


  48 in total

1.  TAPPA: topological analysis of pathway phenotype association.

Authors:  Shouguo Gao; Xujing Wang
Journal:  Bioinformatics       Date:  2007-09-21       Impact factor: 6.937

2.  HISAT: a fast spliced aligner with low memory requirements.

Authors:  Daehwan Kim; Ben Langmead; Steven L Salzberg
Journal:  Nat Methods       Date:  2015-03-09       Impact factor: 28.547

3.  Molecular Pathway Analysis of Mutation Data for Biomarkers Discovery and Scoring of Target Cancer Drugs.

Authors:  Marianna Zolotovskaia; Maxim Sorokin; Andrew Garazha; Nikolay Borisov; Anton Buzdin
Journal:  Methods Mol Biol       Date:  2020

4.  Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies.

Authors:  Rotem Ben-Hamo; Sol Efroni
Journal:  BMC Syst Biol       Date:  2012-01-11

5.  SPIKE--a database, visualization and analysis tool of cellular signaling pathways.

Authors:  Ran Elkon; Rita Vesterman; Nira Amit; Igor Ulitsky; Idan Zohar; Mali Weisz; Gilad Mass; Nir Orlev; Giora Sternberg; Ran Blekhman; Jackie Assa; Yosef Shiloh; Ron Shamir
Journal:  BMC Bioinformatics       Date:  2008-02-20       Impact factor: 3.169

6.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

Authors:  Michael I Love; Wolfgang Huber; Simon Anders
Journal:  Genome Biol       Date:  2014       Impact factor: 13.583

7.  Predicting and affecting response to cancer therapy based on pathway-level biomarkers.

Authors:  Rotem Ben-Hamo; Adi Jacob Berger; Nancy Gavert; Mendy Miller; Guy Pines; Roni Oren; Eli Pikarsky; Cyril H Benes; Tzahi Neuman; Yaara Zwang; Sol Efroni; Gad Getz; Ravid Straussman
Journal:  Nat Commun       Date:  2020-07-03       Impact factor: 14.919

8.  Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs.

Authors:  Marianna A Zolotovskaia; Maxim I Sorokin; Anna A Emelianova; Nikolay M Borisov; Denis V Kuzmin; Pieter Borger; Andrew V Garazha; Anton A Buzdin
Journal:  Front Pharmacol       Date:  2019-01-23       Impact factor: 5.810

9.  In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state.

Authors:  Alexander Aliper; Aleksey V Belikov; Andrew Garazha; Leslie Jellen; Artem Artemov; Maria Suntsova; Alena Ivanova; Larisa Venkova; Nicolas Borisov; Anton Buzdin; Polina Mamoshina; Evgeny Putin; Andrew G Swick; Alexey Moskalev; Alex Zhavoronkov
Journal:  Aging (Albany NY)       Date:  2016-09-24       Impact factor: 5.682

10.  Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.

Authors:  Victor Tkachev; Maxim Sorokin; Constantin Borisov; Andrew Garazha; Anton Buzdin; Nicolas Borisov
Journal:  Int J Mol Sci       Date:  2020-01-22       Impact factor: 5.923

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.