Confidence for exploratory machine learning

Peter Salamon; David Salamon; V. Adrian Cantu; Michelle An; Tyler Perry; Robert A. Edwards; Anca M. Segall

doi:10.1016/j.bpj.2022.11.1601

Confidence for exploratory machine learning

Peter Salamon, David Salamon, V. Adrian Cantu, Michelle An, Tyler Perry, Robert A. Edwards, Anca M. Segall

Research output: Contribution to journal › Meeting Abstract › peer-review

Abstract

Machine learning problems performing classification into categories whose definition and validity come from a necessary dialogue with the machine learning system represent a special class of problems we call “exploratory”. Here we argue that such problems need special treatment and illustrate this to be the case for confidence calibration in gene function prediction.

Original language	English
Article number	1365-Pos
Pages (from-to)	281a
Number of pages	1
Journal	Biophysical journal
Volume	122
Issue number	3 Suppl. 1
DOIs	https://doi.org/10.1016/j.bpj.2022.11.1601
Publication status	Published - 10 Feb 2023
Externally published	Yes
Event	67th Biophysical Society Annual Meeting: Biophysics beyond the boundaries - San Diego, United States Duration: 18 Feb 2023 → 22 Feb 2023 Conference number: 67th

Bibliographical note

Abstract for poster presented at the meeting Monday, February 20, 2023.

Keywords

Machine learning
Gene function prediction.
Confidence calibration

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10.1016/j.bpj.2022.11.1601Licence: In Copyright

Cite this

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title = "Confidence for exploratory machine learning",

abstract = "Machine learning problems performing classification into categories whose definition and validity come from a necessary dialogue with the machine learning system represent a special class of problems we call “exploratory”. Here we argue that such problems need special treatment and illustrate this to be the case for confidence calibration in gene function prediction.",

keywords = "Machine learning, Gene function prediction., Confidence calibration",

author = "Peter Salamon and David Salamon and Cantu, {V. Adrian} and Michelle An and Tyler Perry and Edwards, {Robert A.} and Segall, {Anca M.}",

note = "Abstract for poster presented at the meeting Monday, February 20, 2023.; 67th Biophysical Society Annual Meeting : Biophysics beyond the boundaries, BPS2023 ; Conference date: 18-02-2023 Through 22-02-2023",

year = "2023",

month = feb,

day = "10",

doi = "10.1016/j.bpj.2022.11.1601",

language = "English",

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journal = "Biophysical journal",

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TY - JOUR

T1 - Confidence for exploratory machine learning

AU - Salamon, Peter

AU - Salamon, David

AU - Cantu, V. Adrian

AU - An, Michelle

AU - Perry, Tyler

AU - Edwards, Robert A.

AU - Segall, Anca M.

N1 - Conference code: 67th

PY - 2023/2/10

Y1 - 2023/2/10

N2 - Machine learning problems performing classification into categories whose definition and validity come from a necessary dialogue with the machine learning system represent a special class of problems we call “exploratory”. Here we argue that such problems need special treatment and illustrate this to be the case for confidence calibration in gene function prediction.

AB - Machine learning problems performing classification into categories whose definition and validity come from a necessary dialogue with the machine learning system represent a special class of problems we call “exploratory”. Here we argue that such problems need special treatment and illustrate this to be the case for confidence calibration in gene function prediction.

KW - Machine learning

KW - Gene function prediction.

KW - Confidence calibration

UR - http://www.scopus.com/inward/record.url?scp=85148106318&partnerID=8YFLogxK

UR - https://biophysics.cld.bz/Biophysical-Society-67th-Annual-Meeting-Program-Guide

U2 - 10.1016/j.bpj.2022.11.1601

DO - 10.1016/j.bpj.2022.11.1601

M3 - Meeting Abstract

C2 - 36783394

AN - SCOPUS:85148106318

SN - 1542-0086

VL - 122

SP - 281a

JO - Biophysical journal

JF - Biophysical journal

IS - 3 Suppl. 1

M1 - 1365-Pos

T2 - 67th Biophysical Society Annual Meeting

Y2 - 18 February 2023 through 22 February 2023

ER -

Confidence for exploratory machine learning

Abstract

Bibliographical note

Keywords

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