scholarly journals Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0147475 ◽  
Author(s):  
Hao He ◽  
Shaolong Cao ◽  
Tianhua Niu ◽  
Yu Zhou ◽  
Lan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Mineral Density ◽  
Bone Mineral ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mineral Density ◽  
Multiple Gene ◽  
Meta Analyses

scholarly journals Identification of a key gene module associated with glucocorticoid- induced derangement in bone mineral density in patients with asthma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Suh-Young Lee ◽  
Ha-Kyeong Won ◽  
Byung-Keun Kim ◽  
Sae-Hoon Kim ◽  
Yoon-Seok Chang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Mineral Density ◽  
Bone Mineral ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Z Score ◽  
Gene Module ◽  
Mineral Density ◽  
Gene Modules

AbstractDerangement in bone mineral density (BMD) caused by glucocorticoid is well-known. The present study aimed to find key biological pathways associated with low BMD after glucocorticoid treatment in asthmatics using gene expression profiles of peripheral blood cells. We utilized immortalized B cells (IBCs) from 32 childhood asthmatics after multiple oral glucocorticoid bursts and peripheral blood mononuclear cells (PBMCs) from 17 adult asthmatics after a long-term use of oral glucocorticoid. We searched co-expressed gene modules significantly related with the BMD Z score in childhood asthmatics and tested if these gene modules were preserved and significantly associated with the BMD Z score in adult asthmatics as well. We identified a gene module composed of 199 genes significantly associated with low BMD in both childhood and adult asthmatics. The structure of this module was preserved across gene expression profiles. We found that the cellular metabolic pathway was significantly enriched in this module. Among 18 hub genes in this module, we postulated that 2 genes, CREBBP and EP300, contributed to low BMD following a literature review. A novel biologic pathway identified in this study highlighted a gene module and several genes as playing possible roles in the pathogenesis of glucocorticoid- induced derangement in BMD.

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).

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

scholarly journals MicroPath-A pathway-based pipeline for the comparison of multiple gene expression profiles to identify common biological signatures

2009 ◽  
Vol 02 (02) ◽  
pp. 106-116
Author(s):  
Mohsin Khan ◽  
Chandrasekhar Babu Gorle ◽  
Ping Wang ◽  
Xiao-Hui Liu ◽  
Su-Ling Li
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Multiple Gene

Lack of Transparency in the Meta-Analyses of Dietary and Urinary Sodium and Bone Mineral Density or Risk of Osteoporosis: A Letter to the Journal

2019 ◽  
Vol 38 (8) ◽  
pp. 746-747
Author(s):  
Isabel Cardoso ◽  
Joanna Michalowska ◽  
Sofus C. Larsen ◽  
Bo Abrahamsen ◽  
Berit L. Heitmann ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Bone Mineral ◽  
Urinary Sodium ◽  
Mineral Density ◽  
Meta Analyses

A genomic strategy to refine prognosis in early stage non-small cell lung carcinoma (NSCLC)

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 7026-7026
Author(s):  
D. H. Harpole ◽  
R. Petersen ◽  
S. Mukherjee ◽  
A. Bild ◽  
H. Dressman ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Smoking History ◽  
Histologic Subtype ◽  
Multiple Gene ◽  
Risk Of Recurrence

7026 Background. Although stage-specific classification identifies appropriate populations for adjuvant chemotherapy, this is likely an imprecise predictor for the individual patient with early stage NSCLC. Methods. Using previously-described methodologies that employ DNA microarray data, multiple gene expression profiles (‘metagenes’) that predict risk of recurrence in patients with stage I disease were identified. This analysis used an initial ‘test’ cohort of patients with NSCLC (n = 89) that represented an equal mix of squamous cell and adenocarcinoma. Also, each histologic subset had equal number of patients who survived more than 5 years and those who died within 2.5 years of initial diagnosis. The performance of the metagene-based model generated on the training cohort was then evaluated in independent ‘validation’ sets, including two multi-center cooperative group studies (ACOSOG Z0030 and CALGB 9761). Importantly, the CALGB validation was performed in a blinded fashion. Results. Classification tree analyses that sample multiple gene expression profiles were used to develop a model of recurrence, termed the Lung Metagene Model, that accurately assesses prognosis (risk of recurrence and survival), performing significantly (p<0.001, odds ratio: 16.1, multivariate analysis) better than pathologic stage, T-size, nodal status, age, gender, histologic subtype and smoking history. The accuracy of prognosis using the Lung Metagene Model exceeded 90% (leave-one-out cross validation) in the initial training set (n = 89), 72% in the ACOSOG (n = 25), and 81% in the CALGB (n = 84) datasets. The prognostic accuracy was consistent across histologic subtypes and stages of NSCLC. Importantly, this provides an opportunity to re-classify stage IA patients to identify a subset of ‘high risk’ patients that may benefit from adjuvant chemotherapy. Further, stage IB and II patients identified as ‘low risk’ for recurrence, and who present co-morbidities, could potentially be candidates for observation, and those patients predicted to be at ‘high risk’ may benefit from novel therapeutic trials. Conclusions. The Lung Metagene Model provides a mechanism to refine the estimation of an individual patient’s risk for disease recurrence and thus guide the use of adjuvant chemotherapy in NSCLC. No significant financial relationships to disclose.

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