ArticleG3 (Bethesda, Md.)2025
MLDAAPP: machine learning data acquisition for assessing population phenotypes.
Article in G3 (Bethesda, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Overwintering drives rapid adaptation in Drosophila with potential costs to insecticide resistance.Evolution; international journal of organic evolution · 2026Article
- Microbiome evolution plays a secondary role in host rapid adaptation.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Collecting phenotypic data from many individuals is critical to answering fundamental biological questions, particularly in genetics. Yet, whole organismal phenotypic data are still often collected manually; limiting the scale of data generation, precluding reproducible workflows, and creating potential bias. Computer vision can ameliorate these issues, but currently available packages only operate with specific inputs and have limited scalability. We present Machine Learning Data Acquisition for Assessing Population Phenotypes (MLDAAPP), a package of tools built on YOLOv8 for collecting phenotypic data from groups of individuals that is flexible for generating counts (e.g. Drosophila fecundity, census), movement (e.g. activity, aversive behavior), and many other potential phenotypes. MLDAAPP is both accurate and uniquely effective for obtaining phenotypic data in challenging conditions-particularly images and videos of varying quality derived from both lab and field environments. Employing MLDAAPP solves key issues of reproducibility and increases the scale and scope of data generation.
Indexed as
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What OpenQuestion holds
Registered trials
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.