ArticleNature machine intelligence2019
Towards Algorithmic Analytics for Large-scale Datasets.
Article in Nature machine intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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.
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Who cites it
49 citing papers in PubMed.
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- Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.bioRxiv : the preprint server for biology · 2026Article
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- Gaining Brain Insights by Tapping into the Black Box: Linking Structural MRI Features to Age and Cognition using Shapley-Based Interpretation Methods.Neuroinformatics · 2025Article
- Multimodal population study reveals the neurobiological underpinnings of chronotype.Nature human behaviour · 2025Article
- Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort.PloS one · 2025Article
- Pain can't be carved at the joints: defining function-based pain profiles and their relevance to chronic disease management in healthcare delivery design.BMC medicine · 2024Article
- A TCN-Linear Hybrid Model for Chaotic Time Series Forecasting.Entropy (Basel, Switzerland) · 2024Article
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- Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression.Communications biology · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals.JAMA psychiatry · 2024Article
- Harnessing population diversity: in search of tools of the trade.GigaScience · 2024Article
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.Scientific data · 2023Article
- The end game: respecting major sources of population diversity.Nature methods · 2023Article
- Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence.Nature human behaviour · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The traditional goals of quantitative analytics cherish simple, transparent models to generate explainable insights. Large-scale data acquisition, enabled for instance by brain scanning and genomic profiling with microarray-type techniques, has prompted a wave of statistical inventions and innovative applications. Modern analysis approaches 1) tame large variable arrays capitalizing on regularization and dimensionality-reduction strategies, 2) are increasingly backed up by empirical model validations rather than justified by mathematical proofs, 3) will compare against and build on open data and consortium repositories, as well as 4) often embrace more elaborate, less interpretable models in order to maximize prediction accuracy. Here we review these trends in learning from "big data" and illustrate examples from imaging neuroscience.
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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.