ArticleBiochemical and biophysical research communications2020
Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.
Article in Biochemical and biophysical research communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 86 citations in OpenAlex.
- Network Topology and Interactomic Analysis Reveal the Regulatory Framework of the Humanin Protein Family (MTRNR2Lx Class).Biomolecules · 2026Article
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- KnvResGAT: SARS-CoV-2 sequence classification using k-mer natural vector and graph attention networks.BMC research notes · 2026Article
- Enhanced deep Convolutional Neural Network for SARS-CoV-2 variants classification.Frontiers in artificial intelligence · 2025Article
- Article
- Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study.NPJ digital medicine · 2024Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- TCellR2Vec: efficient feature selection for TCR sequences for cancer classification.PeerJ. Computer science · 2024Article
- Article
- Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19.Research square · 2023Article
- ViralVectors: compact and scalable alignment-free virome feature generation.Medical & biological engineering & computing · 2023Article
- Article
- Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors.Biomolecules · 2023Article
- Protein Fitness Prediction Is Impacted by the Interplay of Language Models, Ensemble Learning, and Sampling Methods.Pharmaceutics · 2023Article
- Benchmarking machine learning robustness in Covid-19 genome sequence classification.Scientific reports · 2023Article
- Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods.Biomedical signal processing and control · 2023Article
- Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Efficient analysis of COVID-19 clinical data using machine learning models.Medical & biological engineering & computing · 2022Article
- AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.BMC bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict host specificity of coronaviruses will be valuable for identifying and controlling future outbreaks. The coronavirus S protein plays a key role in host specificity by attaching the virus to receptors on the cell membrane. We analyzed 1238 spike sequences for their host specificity. Spike sequences readily segregate in t-SNE embeddings into clusters of similar hosts and/or virus species. Machine learning with SVM, Logistic Regression, Decision Tree, Random Forest gave high average accuracies, F
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.