Bayesian neural networks for detecting epistasis in genetic association studies

Background: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. Results: A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. Conclusions: The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0368-0) contains supplementary material, which is available to authorized users

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines

Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online

Trajectories of Big Five Personality Traits: A Coordinated Analysis of 16 Longitudinal Samples

This study assessed change in self‐reported Big Five personality traits. We conducted a coordinated integrative data analysis using data from 16 longitudinal samples, comprising a total sample of over 60 000 participants. We coordinated models across multiple datasets and fit identical multi‐level growth models to assess and compare the extent of trait change over time. Quadratic change was assessed in a subset of samples with four or more measurement occasions. Across studies, the linear trajectory models revealed declines in conscientiousness, extraversion, and openness. Non‐linear models suggested late‐life increases in neuroticism. Meta‐analytic summaries indicated that the fixed effects of personality change are somewhat heterogeneous and that the variability in trait change is partially explained by sample age, country of origin, and personality measurement method. We also found mixed evidence for predictors of change, specifically for sex and baseline age. This study demonstrates the importance of coordinated conceptual replications for accelerating the accumulation of robust and reliable findings in the lifespan developmental psychological sciences. © 2020 European Association of Personality PsychologyPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/1/per2259.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/2/per2259-sup-0001-Data_S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/3/per2259-sup-0002-Open_Practices_Disclosure_Form.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156004/4/per2259_am.pd

Evaluation of a multi-disciplinary back pain rehabilitation programme—individual and group perspectives

To evaluate the impact of a multi-disciplinary back pain rehabilitation programme using a combination of individual and group change data. A total of 261 consecutive patients attending an assessment session for the back pain rehabilitation programme completed the SF-36 health survey questionnaire. The patients were requested to complete the questionnaires again at programme completion and at the 6-month follow-up. The Reliable Change Index was used to define 'clinical significance' in terms of the assessment of individual change. Half of those patients considered to be suitable for the programme subsequently completed it. In group terms, non-completers scored lower than completers on all SF-36 scales. Statistically significant improvements were evident for those completing the programme (all scales at P < 0.000), with improvement maintained at follow-up. In individual terms, 'clinical significance' was exceeded most frequently in the Physical Functioning and Role Physical scales. Whilst some participants lost previous improvements between completion and follow-up, others improved over this same time period. The majority of those completing the programme showed improvement in at least one scale. Adding assessment of individual change to traditional group change measures provides greater insight into the impact a rehabilitation programme has upon participants' quality of life. Whilst the programme is clearly effective for those who complete it, work is required to limit post-programme deterioration and improve uptak