ArticleComputational and structural biotechnology journal2018
iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree.
Article in Computational and structural biotechnology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 113 citations in OpenAlex.
- RSCNN-PseU: random searching-based convolutional neural network model for identifying RNA pseudouridine.Briefings in bioinformatics · 2025Article
- Compositional modeling of solution gas-oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state.Scientific reports · 2025Article
- WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-protein Binding Sites.Current computer-aided drug design · 2025Article
- Predicting Transcription Factor Binding Sites with Deep Learning.International journal of molecular sciences · 2024Article
- StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture.International journal of molecular sciences · 2023Article
- Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.Diagnostics (Basel, Switzerland) · 2023Article
- Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training.Interdisciplinary sciences, computational life sciences · 2023Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- Empirical comparison and recent advances of computational prediction of hormone binding proteins using machine learning methods.Computational and structural biotechnology journal · 2023Review
- Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State.ACS omega · 2022Article
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
- MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.Briefings in bioinformatics · 2022Article
- SAResNet: self-attention residual network for predicting DNA-protein binding.Briefings in bioinformatics · 2021Article
- Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.BMC medical informatics and decision making · 2021Article
- Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.Briefings in bioinformatics · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
- Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes.Frontiers in genetics · 2021Article
- AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.Frontiers in pharmacology · 2021Review
- An improved deep forest model for prediction of e-commerce consumers' repurchase behavior.PloS one · 2021Article
- iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.Computational and mathematical methods in medicine · 2021Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A soluble carrier growth hormone binding protein (GHBP) that can selectively and non-covalently interact with growth hormone, thereby acting as a modulator or inhibitor of growth hormone signalling. Accurate identification of the GHBP from a given protein sequence also provides important clues for understanding cell growth and cellular mechanisms. In the postgenomic era, there has been an abundance of protein sequence data garnered, hence it is crucial to develop an automated computational method which enables fast and accurate identification of putative GHBPs within a vast number of candidate proteins. In this study, we describe a novel machine-learning-based predictor called iGHBP for the identification of GHBP. In order to predict GHBP from a given protein sequence, we trained an extremely randomised tree with an optimal feature set that was obtained from a combination of dipeptide composition and amino acid index values by applying a two-step feature selection protocol. During cross-validation analysis, iGHBP achieved an accuracy of 84.9%, which was ~7% higher than the control extremely randomised tree predictor trained with all features, thus demonstrating the effectiveness of our feature selection protocol. Furthermore, when objectively evaluated on an independent data set, our proposed iGHBP method displayed superior performance compared to the existing method. Additionally, a user-friendly web server that implements the proposed iGHBP has been established and is available at http://thegleelab.org/iGHBP.
Indexed as
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.