ArticleBioinformatics advances2023
MonoNet: enhancing interpretability in neural networks via monotonic features.
Article in Bioinformatics advances, 2023. 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.
- Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets.Scientific data · 2025Article
- Visual Attention Prompted Prediction and Learning.IJCAI : proceedings of the conference · 2024Article
- A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence.Materials (Basel, Switzerland) · 2024Review
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- FLAN: feature-wise latent additive neural models for biological applications.Briefings in bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Being able to interpret and explain the predictions made by a machine learning model is of fundamental importance. Unfortunately, a trade-off between accuracy and interpretability is often observed. As a result, the interest in developing more transparent yet powerful models has grown considerably over the past few years. Interpretable models are especially needed in high-stake scenarios, such as computational biology and medical informatics, where erroneous or biased models' predictions can have deleterious consequences for a patient. Furthermore, understanding the inner workings of a model can help increase the trust in the model. Results: We introduce a novel structurally constrained neural network, Availability and implementation: Code and sample data are available at https://github.com/phineasng/mononet. Supplementary information: Supplementary data are available at
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.