Evaluating biomarker features for lung cancer using machine learning

P. Viswanathan; S. Prabhala; J. Lin; H. K. Roy; H. Subramanian; V. Backman

Evaluating biomarker features for lung cancer using machine learning

P. Viswanathan^*, S. Prabhala, J. Lin, H. K. Roy, H. Subramanian, V. Backman

^*Corresponding author for this work

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Machine learning is being applied to enhance the information garnered from biomarkers that are quantified from buccal samples for determining an individuals’ predisposition to lung cancer using partial wave spectroscopy.

Original language	English (US)
Title of host publication	Bio-Optics
Subtitle of host publication	Design and Application, BODA 2021
Publisher	The Optical Society
ISBN (Electronic)	9781557528209
State	Published - 2021
Event	Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 - Virtual, Online, United States Duration: Apr 12 2021 → Apr 16 2021

Publication series

Name	Optics InfoBase Conference Papers

Conference

Conference	Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021
Country/Territory	United States
City	Virtual, Online
Period	4/12/21 → 4/16/21

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{43e7bcd0db1343e8a4e9c1a408172970,

title = "Evaluating biomarker features for lung cancer using machine learning",

abstract = "Machine learning is being applied to enhance the information garnered from biomarkers that are quantified from buccal samples for determining an individuals{\textquoteright} predisposition to lung cancer using partial wave spectroscopy.",

author = "P. Viswanathan and S. Prabhala and J. Lin and Roy, {H. K.} and H. Subramanian and V. Backman",

note = "Publisher Copyright: {\textcopyright} 2021 The Author (s).; Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021 ; Conference date: 12-04-2021 Through 16-04-2021",

year = "2021",

language = "English (US)",

series = "Optics InfoBase Conference Papers",

publisher = "The Optical Society",

booktitle = "Bio-Optics",

address = "United States",

}

Viswanathan, P, Prabhala, S, Lin, J, Roy, HK, Subramanian, H & Backman, V 2021, Evaluating biomarker features for lung cancer using machine learning. in Bio-Optics: Design and Application, BODA 2021., DF1A.5, Optics InfoBase Conference Papers, The Optical Society, Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021, Virtual, Online, United States, 4/12/21.

TY - GEN

T1 - Evaluating biomarker features for lung cancer using machine learning

AU - Viswanathan, P.

AU - Prabhala, S.

AU - Lin, J.

AU - Roy, H. K.

AU - Subramanian, H.

AU - Backman, V.

PY - 2021

Y1 - 2021

N2 - Machine learning is being applied to enhance the information garnered from biomarkers that are quantified from buccal samples for determining an individuals’ predisposition to lung cancer using partial wave spectroscopy.

AB - Machine learning is being applied to enhance the information garnered from biomarkers that are quantified from buccal samples for determining an individuals’ predisposition to lung cancer using partial wave spectroscopy.

UR - http://www.scopus.com/inward/record.url?scp=85119342358&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85119342358&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85119342358

T3 - Optics InfoBase Conference Papers

BT - Bio-Optics

PB - The Optical Society

T2 - Bio-Optics: Design and Application, BODA 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

Y2 - 12 April 2021 through 16 April 2021

ER -

Evaluating biomarker features for lung cancer using machine learning

Abstract

Publication series

Conference

ASJC Scopus subject areas

UN SDGs

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