FNatPred: A Data-Driven Approach for Distinguishing Between NAT and Tumor on the Fungal Microbiome

Our approach achieved clear separation between tumor and NAT mycobiomes across multiple cancer types, with particularly pronounced discrimination in colorectal cancer (CRC). The proposed model significantly outperformed existing methods in tumor-NAT classification, demonstrating an average AUC improvement of approximately 10%.

Authors

Shankai Yan (ORCID: https://orcid.org/0000-0003-0369-4979)
Yang Xin (ORCID: https://orcid.org/0000-0002-8315-8000)
Buchao Zhan (ORCID: https://orcid.org/0009-0008-4129-7281)
Dongmei He (ORCID: https://orcid.org/0009-0000-3965-2557)
Zilong Zhang

Institutions

Qinghai University (CN)
Hainan University (CN)
Hainan Agricultural School (CN)

Publication Details

Journal: IEEE Transactions on Computational Biology and Bioinformatics
Published: 2025-12-03
DOI: https://doi.org/10.1109/tcbbio.2025.3639775
Primary Topic: Gene expression and cancer classification
Type: article
Field-Weighted Citation Impact: 0.00

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article

FNatPred: A Data-Driven Approach for Distinguishing Between NAT and Tumor on the Fungal Microbiome

Shankai Yan, Yang Xin, Buchao Zhan, Dongmei He et al.

Dec 3, 2025

IEEE Transactions on Computational Biology and Bioinformatics

Gene expression and cancer classification

article

FNatPred: A Data-Driven Approach for Distinguishing Between NAT and Tumor on the Fungal Microbiome

Shankai Yan, Yang Xin, Buchao Zhan, Dongmei He, Zilong Zhang

December 3, 2025article en

Abstract

Our approach achieved clear separation between tumor and NAT mycobiomes across multiple cancer types, with particularly pronounced discrimination in colorectal cancer (CRC). The proposed model significantly outperformed existing methods in tumor-NAT classification, demonstrating an average AUC improvement of approximately 10%.

IEEE Transactions on Computational Biology and BioinformaticsVol. 23(1)

Qinghai University (CN), Hainan University (CN), Hainan Agricultural School (CN)

Openalex Percentile: Top 65%

Gene expression and cancer classification

DOI Source OpenAlex

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