ArticleNature communications2021
Supervised dimensionality reduction for big data.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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Who cites it
25 citing papers in PubMed.
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Large-sample PCA eigenvectors stabilize cortical thickness components and improve small sample brain behavior prediction.Scientific reports · 2026Article
- A transfer learning-based multimodal model for early prediction of 90-day respiratory failure in dermatomyositis-associated interstitial lung disease.Frontiers in immunology · 2026Article
- Time Series Data Mining for Linking the Shape of Bacterial Growth Curves to Biological Functions.Computational and structural biotechnology journal · 2026Article
- Research on Remaining Useful Life Prediction of PEMWE Stack Based on the LSTM-iTransformer Model.ACS omega · 2025Article
- Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025Review
- Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.Nature communications · 2025Article
- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- Reducing phenotype-structured partial differential equations models of cancer evolution to systems of ordinary differential equations: a generalised moment dynamics approach.Journal of mathematical biology · 2025Article
- WheatGP, a genomic prediction method based on CNN and LSTM.Briefings in bioinformatics · 2025Article
- LD-informed deep learning for Alzheimer's gene loci detection using WGS data.medRxiv : the preprint server for health sciences · 2024Article
- CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes.Database : the journal of biological databases and curation · 2024Article
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.The Journal of international medical research · 2024Article
- GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning.Scientific reports · 2024Article
- Using slisemap to interpret physical data.PloS one · 2024Article
- Improving reduced-order models through nonlinear decoding of projection-dependent outputs.Patterns (New York, N.Y.) · 2023Article
- DNA Methylation-Based Testing in Peripheral Blood Mononuclear Cells Enables Accurate and Early Detection of Colorectal Cancer.Cancer research · 2023Article
- Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.The British journal of radiology · 2023Review
- First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa.Nature communications · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.
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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.