Graphene and metal–organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

Tran, Anh Tuan Trong; Hassan, Kamrul; Tung, Tran Thanh; Tripathy, Ashis; Mondal, Ashok; Losic, Dusan

doi:10.1039/d4nr00174e

Nanoscale

2024

DOI: 10.1039/d4nr00174e

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Graphene and metal–organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

Anh Tuan Trong Tran,

Kamrul Hassan,

Tran Thanh Tung

et al.

Abstract: Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high...

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References 47 publications

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

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Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

View full text Add to dashboard Cite

show abstract

Recent advances in biosensors detecting biomarkers from exhaled breath and saliva for respiratory disease diagnosis

Xiong,

Zhang,

Sun

et al. 2025

Biosensors and Bioelectronics

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Graphene and metal–organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation

Abstract: Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high...

Cited by 2 publications

References 47 publications

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Recent advances in biosensors detecting biomarkers from exhaled breath and saliva for respiratory disease diagnosis

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