Meta analysis approaches for big questions with big data.

January 3-7, 2022

The Big Island of Hawaii

Topics can include but are not limited to:

• Pairing meta-analysis methods with other machine and deep learning techniques

• Meta-analysis methodologies in genomic, transcriptomic, and ‘omics data

• Advanced data visualization for meta-analysis methods

• Computational algorithms and simulation studies to support meta-analysis

• Meta-analysis using networks and graph theory

• Multimodal ‘omics studies

High dimensional biological and ‘omics datasets present unique challenges with traditional meta-analysis approaches and are in need of innovation in computational methodology.  In this session we are seeking papers which employ meta-analysis methodologies in applications related to biology and health as well as papers posing new meta-analysis approaches. 

We also ask that authors reflect upon how publication bias and the peer review process may have influenced their findings in the discussion section of their manuscript.

Note: Manuscripts submitted to this session will be undergoing a crowd peer review process. As part of this process, manuscripts will be reviewed by a wide diversity of scholars and professionals and reviewers will have the opportunity to interact and discuss feedback with one another. Authors will receive all discussions and final recommendations from their team of reviewers. In exchange for this highly interactive review process, we ask authors to add an acknowledgement section entitled ‘Reviewer Contributors’ to attribute credit to participating reviewers. 

Papers submitted to the Human Intrigue section at PSB will undergo a crowd review process. 

The traditional peer preview process has many widely accepted draw backs, is inefficient, and introduces bias and systemic discrimination. This is particularly true in multi-disciplinary research, where it is increasingly difficult to identify reviewers who are qualified to provide feedback on entire manuscripts.

The crowd review method has many merits. One, it reduces the workload for any individual reviewer and hence mitigates reviewer fatigue. Two, it allows reviewers to focus on sections of a manuscript they are qualified to give feedback on. Three, due to the nature of the scientific discussion, the feedback tends to be stronger and reach an informative consensus among reviewers. Four, a greater diversity of reviewers can be included, improving the overall quality of a final manuscript. 

We are establishing a crowd review platform that would allow reviewers to engage in discussion about manuscripts through a comment section to allow debate back and forth. We are committing to the following principles of crowd peer review:

• We will seek a wide diversity of reviewers for each submission
• Reviewers will have the opportunity to interact with one another in order to agree on critical changes and provide a variety of feedback for authors
• Reviewers will be credited for their contributions on a separate acknowledgement section the conference proceedings we’re calling ‘Reviewer Contributors’

Interested in this collaborative review process? Let us know below:

Dartmouth College

Dartmouth College

Dartmouth College

Industry (HigherMe)

Stanford School of Medicine

University of Colorado School of Medicine

University of Pennsylvania

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