ArticlePloS one2015
Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 154 citations in OpenAlex.
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- Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics.Nature communications · 2026Article
- ClusterGraph: a new tool for visualisation and compression of multidimensional data.GigaScience · 2026Article
- Contrastive independent component analysis for salient patterns and dimensionality reduction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Review
- The Rayleigh Quotient and Contrastive Principal Component Analysis I.bioRxiv : the preprint server for biology · 2025Article
- Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence.International journal of molecular sciences · 2025Article
- Contrastive dimension reduction: when and how?Advances in neural information processing systems · 2024Article
- PROBABILISTIC CONTRASTIVE DIMENSION REDUCTION FOR CASE-CONTROL STUDY DATA.The annals of applied statistics · 2024Article
- Article
- Anomaly detection in mixed high-dimensional molecular data.Bioinformatics (Oxford, England) · 2023Article
- Multi-scale affinities with missing data: Estimation and applications.Statistical analysis and data mining · 2022Article
- LassoNet: Neural Networks with Feature Sparsity.Proceedings of machine learning research · 2021Article
- A comprehensive empirical comparison of hubness reduction in high-dimensional spaces.Knowledge and information systems · 2019Article
- Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform.Plant methods · 2019Article
- In silico identification of critical proteins associated with learning process and immune system for Down syndrome.PloS one · 2019Article
- Bi-stream CNN Down Syndrome screening model based on genotyping array.BMC medical genomics · 2018Article
- Understanding diseases as increased heterogeneity: a complex network computational framework.Journal of the Royal Society, Interface · 2018Article
- Exploring patterns enriched in a dataset with contrastive principal component analysis.Nature communications · 2018Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
Abstract
Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.
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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.