ArticleMethods in molecular biology (Clifton, N.J.)2026
Using Biological Networks to Guide Biomedical Prediction.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
6 authors.
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Abstract
Predictive modeling is a transformative tool for understanding complex biological systems and advancing biomedical discovery. A central challenge is ensuring that predictive models are not only accurate but also biologically interpretable. One way to address this challenge is through network-guided approaches, which integrate prior biological knowledge into bioinformatic algorithms and model architectures. By aligning predictive models with biological networks at various scales, these approaches can improve biological interpretability while maintaining strong predictive performance. In this chapter, we introduce the fundamentals of biological networks and describe strategies for incorporating networks into modeling frameworks for predicting biomedically relevant phenotypes. These concepts are explored via a case study of network-guided drug response prediction, in which hierarchical knowledge of cell biology is instrumental to achieving interpretable predictions and biological insights into chemoresistance.
Indexed as
Identifiers
42680988What Socratic 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 Socratic graph.