scholarly journals Machine Learning Analysis of the Bleomycin Mouse Model Reveals the Compartmental and Temporal Inflammatory Pulmonary Fingerprint

iScience ◽  
2020 ◽  
Vol 23 (12) ◽  
pp. 101819
Author(s):  
Natalie Bordag ◽  
Valentina Biasin ◽  
Diana Schnoegl ◽  
Francesco Valzano ◽  
Katharina Jandl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mouse Model ◽  
Learning Analysis

scholarly journals Machine learning analysis of the bleomycin-mouse model reveals the compartmental and temporal inflammatory pulmonary fingerprint

2020 ◽  
Author(s):  
Natalie Bordag ◽  
Valentina Biasin ◽  
Diana Schnoegl ◽  
Francesco Valzano ◽  
Katharina Jandl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mouse Model ◽  
Inflammatory Cell ◽  
Immune Cell ◽  
Close Association ◽  
Data Modelling ◽  
Flow Cytometry Data ◽  
Inflammatory Profile ◽  
Learning Analysis ◽  
Historical Flow

SummaryThe bleomycin mouse-model is the extensively used model to study pulmonary fibrosis, however, the inflammatory cell kinetics and their compartmentalisation is still incompletely understood. Here we assembled historical flow cytometry data, totalling 303 samples and 16 inflammatory-cell populations, and applied advanced data modelling and machine learning methods to conclusively detail these kinetics.Three days post-bleomycin, the inflammatory profile was typified by acute innate inflammation, pronounced neutrophilia, especially of SiglecF+ neutrophils, and alveolar macrophage loss. Between 14 and 21 days, rapid-responders were increasingly replaced by T and B cells, and monocyte-derived alveolar macrophages. Multi-colour imaging revealed the spatial-temporal cell distribution and the close association of T cells with deposited collagen.Unbiased immunophenotyping and data modelling exposed the dynamic shifts in immune-cell composition over the course of bleomycin-triggered lung injury. These results and workflow provides a reference point for future investigations, and can easily be applied in the analysis of other datasets.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

2021 ◽  
Author(s):  
John J. Squiers ◽  
Jeffrey E. Thatcher ◽  
David Bastawros ◽  
Andrew J. Applewhite ◽  
Ronald D. Baxter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Wound Healing ◽  
Multispectral Imaging ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Learning Analysis

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

Sign in / Sign up

Export Citation Format

Share Document