ArticleBiophysical journal2021
Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations.
Article in Biophysical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 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
14 citing papers in PubMed, 23 citations in OpenAlex.
- Functional Calmodulin States Are Selected from an Electrostatically Tuned Free Energy Landscape.Journal of chemical information and modeling · 2026Article
- Combining Molecular Dynamics and Machine Learning to Predict Drug Resistance Causing Variants of BRAF in Colorectal Cancer.Molecules (Basel, Switzerland) · 2025Article
- Dynamicasome-a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations.Communications biology · 2025Article
- Structural Analysis of Amylin and Amyloid β Peptide Signaling in Alzheimer's Disease.Biomolecules · 2025Article
- Novel gene-specific Bayesian Gaussian mixture model to predict the missense variants pathogenicity of Sanfilippo syndrome.Scientific reports · 2024Article
- Protein-folding chaperones predict structure-function relationships and cancer risk in BRCA1 mutation carriers.Cell reports · 2024Article
- Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method.International journal of molecular sciences · 2024Article
- Integration of deep learning with Ramachandran plot molecular dynamics simulation for genetic variant classification.iScience · 2023Article
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
- Understanding the need for digital twins' data in patient advocacy and forecasting oncology.Frontiers in artificial intelligence · 2023Article
- Understanding Calmodulin Variants Affecting Calcium-Dependent Inactivation of L-Type Calcium Channels through Whole-Cell Simulation of the Cardiac Ventricular Myocyte.Biomolecules · 2022Article
- Active site prediction of phosphorylated SARS-CoV-2 N-Protein using molecular simulation.Informatics in medicine unlocked · 2022Article
- Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action.Biomolecules · 2021Article
- Cardiac Alternans Occurs through the Synergy of Voltage- and Calcium-Dependent Mechanisms.Membranes · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.
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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.