Evidence map›Paper›PMID 38464295›Full record

ArticlebioRxiv : the preprint server for biology2024

Evaluating generalizability of artificial intelligence models for molecular datasets.

Yasha Ektefaie, Andrew Shen, Daria Bykova, Maximillian Marin, Marinka Zitnik, Maha Farhat

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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, 10 citations in OpenAlex.

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

6 authors at 4 institutions in 1 country.

Yasha EktefaieDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-2759-4470
Andrew ShenDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Daria BykovaDepartment of Biological Sciences, Columbia University, New York, NY, USA.
Maximillian MarinDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Marinka ZitnikDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8530-7228
Maha FarhatDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Harvard University · USBroad Institute · USColumbia University · USNorthwestern University · US

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamicsR01AI155765 · NIAID · HARVARD MEDICAL SCHOOL · PI FARHAT, MAHA · 2020 to 2024
$3.7M
Measuring Neonatal RegionalizationR01HD108794 · NICHD · STANFORD UNIVERSITY · PI Jochen Profit, JEANNETTE A ROGOWSKI · 2023 to 2026
$2.8M
NHGRI NIH HHS T32 HG002295NIAID NIH HHS R01 AI155765NICHD NIH HHS R01 HD108794
6 · The paper itself

Abstract

Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap,

Identifiers

PMID38464295
PMCPMC10925170
OpenAlexW4392344527

What OpenQuestion 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 OpenQuestion graph.