Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

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Title

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

Content partner

University of Otago

Collection

Otago University Research Archive

Description

Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that...

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Format

Research paper

Research format

Scholarly text / Journal article

Thesis level

Article

Date created

2023-07-11

Creator

Kolisnik, Tyler / Sulit, Arielle Kae / Schmeier, Sebastian / Frizelle, Frank / Purcell, Rachel / Smith, Adam / Silander, Olin

URL

https://hdl.handle.net/10523/24262

Related subjects

Adult / Aged / Aged, 80 and over / Colorectal Neoplasms - genetics / Female / Genetic Markers / Humans / Machine Learning / Male / Microbiota - genetics / Middle Aged / Random Forest

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