ArticleComputational and structural biotechnology journal2021
MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 31 citations in OpenAlex.
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- DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.BMC bioinformatics · 2024Article
- DPI_CDF: druggable protein identifier using cascade deep forest.BMC bioinformatics · 2024Article
- Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities.International journal of molecular sciences · 2024Review
- Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.ACS omega · 2024Review
- Missense3D-TM: Predicting the Effect of Missense Variants in Helical Transmembrane Protein Regions Using 3D Protein Structures.Journal of molecular biology · 2024Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- Accurate few-shot object counting with Hough matching feature enhancement.Frontiers in computational neuroscience · 2023Article
- An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction.Frontiers in computational neuroscience · 2023Article
- VariBench, new variation benchmark categories and data sets.Frontiers in bioinformatics · 2023Article
- Identifying Transcription Factors That Prefer Binding to Methylated DNA Using ReducedACS omega · 2022Article
- The Future of Genetic Disease Studies: Assembling an Updated Multidisciplinary Toolbox.Frontiers in cell and developmental biology · 2022Article
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
Abstract
Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (
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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.