Commun Biol February 2023 PMID 36841852

Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously.

Steven M Foltz, Casey S Greene, Jaclyn N Taroni

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Peak: 10 in Mar 2023

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What people are saying

The discussions reflect strong support and enthusiasm, with phrases like 'effective cross-platform normalization' and references to 'machine learning applications' showing appreciation for the technical advances. The tone underscores both interest and belief in the high impact of the research, with examples illustrating how the methods enable more accurate data integration in biomedical studies.

AgreementModerate agreement

Most discussions recognize the publication's findings as valuable, highlighting effective integration methods for genomic data, which they generally support.

InterestHigh interest

The comments demonstrate high interest, with many emphasizing the significance of combining microarray and RNA-seq data for advanced research and machine learning.

EngagementHigh engagement

Multiple discussions delve into technical aspects, mentioning specific algorithms, normalization techniques, and potential applications, reflecting deep engagement.

ImpactHigh impact

Participants view this research as highly impactful, often describing it as a 'game changer' or essential for future biomedical data analysis, emphasizing its transformative potential.

Abstract in brief

Cross-platform normalization techniques like quantile normalization and Training Distribution Matching enable the combined use of microarray and RNAseq data for machine learning, addressing differences in data structure and distribution.
Supervised and unsupervised machine learning evaluations show that certain normalization methods can effectively facilitate the integration of the two platforms for predictive modeling.
Alternative methods such as nonparanormal normalization and z-scores are also useful in specific contexts, like pathway analysis with PLIER, demonstrating the potential for combining diverse gene expression datasets in biomedical research.

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Cross-Platform Normalization for Microarray and RNAseq Data in Machine Learning

Cross-platform normalization techniques like quantile normalization and Training Distribution Matching enable the combined use of microarray and RNAseq data for machine learning, addressing differences in data structure…

Social RTCL TV · Jun 28, 2023 29 views

Cannock Private Limited

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...https://t.co/5N2bHHC1Vw #AICompany #CannockPrivateLimited

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Cannock Private Limited

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c We used three supervised algorithms to train classifiers (molecular subtype and mutation status of TP53 and PIK3CA in both BRCA and GBM) on each training set and tested on the microarray and RNA-seq test sets. ... https://t.co/5N2bHHC1Vw

Mar 8, 2023 · 25 followers View post →

Joel Atallah

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