ArticleIn silico pharmacology2021
Comparative performance of extreme learning machine and Hammerstein-Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of
Article in In silico pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Prediction of cell migration potential on human breast cancer cells treated with Albizia lebbeck ethanolic extract using extreme machine learning.Scientific reports · 2023Article
- Evaluation of Anti-Diarrheal Activities of the 80% Methanol Extract and Solvent Fractions ofJournal of experimental pharmacology · 2023Article
- Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique.Life (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
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Abstract
In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein-Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of
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