ArticleiScience2025
MORF: Multi-view oblique random forest for hepatotoxicity prediction.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A dual granular balanced deep forest model for effective drug combination prediction.iScience · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatotoxicity prediction by considering each type of feature as an independent view. The Householder transformation is employed to get the inclined cut hyperplane in each view. Two versions of the multi-view oblique decision tree (ODT) algorithms were designed by generating optimal nodes based on selecting proper hyperplanes from different views, named ODT-N and ODT-R. These two types of ODT algorithms serve as the base learners to construct two MORF. Experiments conducted on the hepatotoxicity data provide performance comparisons among different algorithms, and the results confirm that our algorithms can fully utilize the information in different views.
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.