ArticleInternational journal of molecular sciences2018
Comparative Evaluation of Machine Learning Strategies for Analyzing Big Data in Psychiatry.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- A multi-task learning approach combining regression and classification tasks for joint feature selection.Scientific reports · 2026Article
- Article
- Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research.Environment international · 2023Article
- dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.Bioinformatics (Oxford, England) · 2022Article
- A Perspective: Challenges in Dementia Research.Medicina (Kaunas, Lithuania) · 2022Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
- [Cross-sectoral therapeutic concepts and innovative technologies: new opportunities for the treatment of patients with mental disorders].Der Nervenarzt · 2022Review
- An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data.Frontiers in genetics · 2022Article
- Advancing translational research in neuroscience through multi-task learning.Frontiers in psychiatry · 2022Review
- Pan-cancer classification by regularized multi-task learning.Scientific reports · 2021Article
- Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms.JMIR mental health · 2021Review
- Molecular Psychiatry: Trends and Study Examples.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The requirement of innovative big data analytics has become a critical success factor for research in biological psychiatry. Integrative analyses across distributed data resources are considered essential for untangling the biological complexity of mental illnesses. However, little is known about algorithm properties for such integrative machine learning. Here, we performed a comparative analysis of eight machine learning algorithms for identification of reproducible biological fingerprints across data sources, using five transcriptome-wide expression datasets of schizophrenia patients and controls as a use case. We found that multi-task learning (MTL) with network structure (MTL_NET) showed superior accuracy compared to other MTL formulations as well as single task learning, and tied performance with support vector machines (SVM). Compared to SVM, MTL_NET showed significant benefits regarding the variability of accuracy estimates, as well as its robustness to cross-dataset and sampling variability. These results support the utility of this algorithm as a flexible tool for integrative machine learning in psychiatry.
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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.