Trupti Joshi

ORCID: 0000-0001-8944-4924 · 5 papers in corpus · active 2017-2020

Study types

  • article 5

Condition tags

  • endometriosis 5
article 2020
GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

score (0.918) for the methylomics dataset. We hope in the future a less invasive biopsy can be used to diagnose endometriosis using the findings from such ensemble machine learning classifiers, as demonstrated in this study.

article 2019
Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data
Frontiers in genetics ·doi:10.3389/fgene.2019.00766

Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide, and causing significant impact on their quality of life and economic burden. Neither a definitive clin…

article 2018
A Data Mining Approach for Biomarker Discovery Using Transcriptomics in Endometriosis
·doi:10.1109/bibm.2018.8621150

Endometriosis is a complex and common gynecological disorder affecting 5-10% of reproductive age women. Due to the lack of definitive diagnostic symptoms and expensive invasive procedures for diagnosing endometriosis, the average time for t…

article 2018
Integrative Analysis of DNA Methylation and RNA-Seq Data for Biomarker Detection of Endometriosis.
article 2017
A multi-omics informatics approach for identifying molecular mechanisms and biomarkers in clinical patients with endometriosis
·doi:10.1109/bibm.2017.8218003

Endometriosis is a complex gynecological disorder. The diagnostic process of endometriosis involves an invasive procedure thus delaying the diagnosis for about 10 years on average. Both DNA-methylation data and RNA-seq data has the potentia…