ReviewBrain informatics2015
A review of heterogeneous data mining for brain disorder identification.
Review in Brain informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Stress-associated alterations in amygdala-striatal activity: a multi-level analysis of distributional, dynamical, and computational signatures.Journal of computational neuroscience · 2026Article
- Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis.Frontiers in neurology · 2024Article
- Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study.Brain informatics · 2022Article
- Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network.Frontiers in neuroscience · 2022Article
- Brain functional network modeling and analysis based on fMRI: a systematic review.Cognitive neurodynamics · 2021Review
- Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.Computational and mathematical methods in medicine · 2020Article
- Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease: a comparative study.Medical & biological engineering & computing · 2019Article
- Identifying HIV-induced subgraph patterns in brain networks with side information.Brain informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses many unique challenges for data mining research. For example, the raw data generated by neuroimaging experiments is in tensor representations, with typical characteristics of high dimensionality, structural complexity, and nonlinear separability. Furthermore, brain connectivity networks can be constructed from the tensor data, embedding subtle interactions between brain regions. Other clinical measures are usually available reflecting the disease status from different perspectives. It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis. In this paper, we review some recent data mining methods that are used for analyzing brain disorders.
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.