Evidence map›Paper›PMID 41000811›Full record

ArticlebioRxiv : the preprint server for biology2025

Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.

Maxwell A Konnaris, Manan Saxena, Nicole Lazar, Justin D Silverman

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Maxwell A KonnarisProgram in Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA.ORCID 0000-0003-4893-1413
Manan SaxenaProgram in Informatics, Pennsylvania State University, University Park, PA, USA.
Nicole LazarDepartment of Statistics, Pennsylvania State University, University Park, PA, USA.
Justin D SilvermanCollege of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA.ORCID 0000-0002-3063-2098

Funding

Integrative Analysis of Metabolic Phenotypes (IAMP) Predoctoral Training ProgramT32DK120509 · NIDDK · PENNSYLVANIA STATE UNIVERSITY, THE · PI PATTERSON, ANDREW, PERDEW, GARY H. · 2020 to 2024
$781k
DMS/NIGMS 1: Addressing Measurement Limitations for Sequence Count DataR01GM148972 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI SILVERMAN, JUSTIN D · 2022 to 2024
$600k
NIDDK NIH HHS T32 DK120509NIGMS NIH HHS R01 GM148972
6 · The paper itself

Abstract

Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, often untestable assumptions that can bias inference. Experimental measurements of load (e.g., qPCR, flow cytometry) offer a solution, but remain costly and uncommon. A recent high-profile study proposed that machine learning could bypass this limitation by predicting microbial load from sequencing data alone. To evaluate this claim, we assembled

Indexed as

16S rRNA-seqCovariate ShiftMachine LearningMetagenomicsMicrobiome DataPartially Identified ModelsScale Reliant InferenceSequence Count DataTotal Microbial LoadUncertainty Quantification

Identifiers

PMID41000811
PMCPMC12458437

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.