ArticleCancers2020
Machine Learning Based Analysis of Human Serum
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 12 citations in OpenAlex.
- Network-based single-cell analysis identifies a proliferative GZMBJournal of translational medicine · 2026Article
- Beyond the Scan: Adapting Multimodal Lung Cancer Screening for Central and Eastern Europe-Overcoming Systemic Barriers and Epidemiological Confounders.Medical sciences (Basel, Switzerland) · 2026Review
- Plasma ofCS-modified CD44 predicts the survival of patients with lung cancer.Cancer science · 2024Observational
- Article
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
The human serum
Indexed as
Identifiers
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.