ArticleBioresources and bioprocessing2026
Anti-angiogenesis effect of Baiying Juhua Decoction on the non-small cell lung cancer: integrating pharmacology, multi-machine learning and experimental investigation.
Article in Bioresources and bioprocessing, 2026. 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.
- Unveiling the Immunopharmacological Mechanisms of Biejiajian Pills (BJJP) Underlying the Treatment of Hepatic Fibrosis: Insights From Network Pharmacology and Experimental Validation.Food science & nutrition · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Baiying Juhua Decoction (BYJHD) is a well-established traditional Chinese herbal formula primarily composed of Solanum lyratum and chrysanthemum, which necessitates a thorough investigation to clarify its mechanisms in combating non-small cell lung cancer (NSCLC). This study employed a combination of network pharmacology predictions, serum pharmacochemistry analysis, and various machine learning algorithms (including LASSO, SVM-RFE, and RF) to identify 38 bioactive compounds that target 653 proteins associated with NSCLC. A cross-analysis of 2161 differentially expressed genes (DEGs) and 3124 functional modules led to the identification of 54 critical therapeutic targets. Following this, protein-protein interaction (PPI) and machine learning analysis pinpointed five key signaling regulators. Molecular docking studies demonstrated strong binding affinities between four representative compounds from BYJHD and these targets. Both in vitro and in vivo experiments confirmed that BYJHD inhibits the progression of NSCLC by exerting anti-angiogenic effects, specifically through the inhibition of the ACVRL-1/Smad/ID-1 signaling pathway and the downregulation of CD34. These findings effectively connect traditional clinical applications with contemporary mechanistic insights, positioning BYJHD as a promising multi-target therapeutic candidate for NSCLC.
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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.