ArticleBioinformation2017
Quantitative Structure Activity Relationship study of the Anti-Hepatitis Peptides employing Random Forests and Extra-trees regressors.
Article in Bioinformation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed, 41 citations in OpenAlex.
- Prediction of hemolytic peptides and their hemolytic concentration.Communications biology · 2025Article
- Predicting potential areas at risk of the Dengue Hemorrhagic Fever in Jakarta, Indonesia-analyzing the accuracy of predictive hot spot analysis in the absence of small geographical area data.Infection ecology & epidemiology · 2023Article
- Machine Learning Prediction of Adenovirus D8 Conjunctivitis Complications from Viral Whole-Genome Sequence.Ophthalmology science · 2022Article
- Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model.Pharmaceutics · 2022Article
- A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium.Scientific reports · 2022Article
- Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images.Quantitative imaging in medicine and surgery · 2019Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.
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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 OpenQuestion graph.