Feature Down-Selection to Improve Supervised Classification by Machine Learning on Mass Spectrometry Imaging Data.

Braysen Miller, Aleesa E Chua, Madeline Isom, Eden P Go, Emily R Sekera, Amanda B Hummon, Heather Desaire

Abstract read

In one paragraph

Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Braysen MillerDepartment of Chemistry, University of Kansas, 1450 Jayhawk Blvd, Lawrence, KS 66045, USA.ORCID 0009-0005-4025-494X

Aleesa E ChuaDepartment of Chemistry, University of Kansas, 1450 Jayhawk Blvd, Lawrence, KS 66045, USA.ORCID 0009-0003-7067-3032

Madeline IsomDepartment of Chemistry, University of Kansas, 1450 Jayhawk Blvd, Lawrence, KS 66045, USA.

Eden P GoDepartment of Chemistry, University of Kansas, 1450 Jayhawk Blvd, Lawrence, KS 66045, USA.

Emily R SekeraDepartment of Chemistry and Biochemistry, The Ohio State University, 281 W Lane Ave, Columbus, OH 43210, USA.ORCID 0000-0002-1668-3227

Amanda B HummonDepartment of Chemistry and Biochemistry, The Ohio State University, 281 W Lane Ave, Columbus, OH 43210, USA.

Heather DesaireDepartment of Chemistry, University of Kansas, 1450 Jayhawk Blvd, Lawrence, KS 66045, USA.ORCID 0000-0002-2181-0112

Funding

NIH HHS 1R35GM158423-01NIH HHS 5R01AG072760-03

6 · The paper itself

Abstract

The advancements made in the mass spectrometry imaging (MSI) field have allowed for the generation of very large-scale data sets. These data are often interrogated by machine learning (ML), although storing and handling data sets of this size can be difficult. To aid impacted researchers, we seek to evaluate feature reduction strategies that will minimize the amount of data stored while still maintaining the ability to correctly classify the data. Two different feature selection strategies are tested on six different data sets, leveraging XGBoost as the machine learning algorithm. The study provides evidence that selecting features based on the greatest average abundance across all samples is best suited to scale down the feature set at a more modest trimming level, while selecting features based on statistical analysis via a Student's

Indexed as

data analysisfeature selectionlipidsmachine learningmass spectrometrymass spectrometry imagingspheroids

Identifiers

PMID42357475

DOI10.3390/molecules31122077

PMCPMC13304621

What Socratic holds

Textmetadata

LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.