Dr. Hongtu Zhu is a professor in the Department of Biostatistics at the University of North Carolina Chapel Hill. His background is in statistics, biostatistics, medical imaging, genetics, and computational neuroscience. Dr. Zhu's previous work at Columbia University and New York State Psychiatric Institute included developing new statistical methods for analysis of medical imaging data including MRI, DTI, EEG/MEG, Ultra Sound, and PET. He will be speaking about his recent paper on imaging GWAS in Science. Learn more about his work.
Imaging genomics, big data, electronic health records and data sharing research present challenges to the biocomputing community. The scale and complexity of multidimensional imaging and omics data provide unprecedented opportunities to understand the systems biology and etiology of complex disorders. Big data and data sharing approaches provide large and powerful datasets to ensure that findings are generalizable and replicable. Challenges in assembling, homogenizing, and sharing of data and performing analyses in such datasets will be the focus of this session. We expect a stimulating discussion on how to evolve this field to fulfil the national health priority areas including precision medicine, the BRAIN Initiative, the Big Data to Knowledge (BD2K) initiative, Autism Centers of Excellence (ACE), and the National Alzheimer's Project Act (NAPA).
The scale and complexity of multidimensional imaging and omics data provide unprecedented opportunities in enhancing mechanistic understanding of complex disorders such as neuropsychiatric and neurometabolic illnesses and cancers, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress. However, due to the high dimensionality and complex structure of these data sets, this field faces major computational and bioinformatics challenges. We welcome submissions that address these challenges.
Topics include but are not limited to:
o Methods for genetic, epistatic, genomic or multi-omic analysis of imaging phenotypes
o Methods for quantifying and exploring multidimensional individual genetic and phenotypic vulnerability as well as electronic health records
o Novel methods to handle incomplete data, perform data harmonization across cohorts and modalities, and integrate multi-cohort data
o Novel methods that handle the ultra-high dimensionality and resolution of whole genome/exome sequences (WGS/WES) as well as detect both common and rare genetic variants related to imaging traits
o Novel scalable statistical and machine learning strategies to support large scale consortium-based collaborative efforts (e.g., cloud-based computing and informatics tools to analyze large-scale data in the cloud, and distributed computation methods to handle decentralized datasets)
o Reports on reproducibility and replication of disorder-related findings and patterns in Big Data projects
o Reports on translating Big Data derived measurements to the level of individual subjects by deriving novel phenotypes, guiding diagnostic and treatment decisions
o Predictive and causative modeling and computational approaches for Big Data imaging genetic analyses
August 2, 2021 Paper submissions due
September 13, 2021 Notification of paper acceptance
October 1, 2021 Camera-ready final paper due
December 6, 2021 Deadline for poster abstracts
January 3-7, 2022 PBS 2022 on the Big Island
See http://psb.stanford.edu/keydates/ for more information about registration and travel award applications.
Submission: Please submit online following the instructions at http://psb.stanford.edu/psb-online/psb-submit/.
Paper Format: Papers should be limited to 12 pages (not including the cover letter) and formatted according to the instructions at http://psb.stanford.edu/psb-online/psb-submit/. The bibliography is included in the page limit. The accepted file format is PDF (Adobe Acrobat preferred). Attached files should be named with the last name of the first author (e.g. altman.pdf). Hardcopy submissions or unprocessed TEX or LATEX files or electronic submissions not submitted through the paper management system will be rejected without review.
Cover Letter: Each paper must be accompanied by a cover letter as the first page of your paper submission.
The cover letter must include the following:
o The email address of the corresponding author
o The specific PSB session that should review the paper or abstract
o The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere
o All co-authors concur with the contents of the paper
Paper Review: Please note that the submitted papers are peer-reviewed and accepted on a competitive basis.
Proceedings: The core of the conference consists of rigorously peer-reviewed full-length papers reporting on original work. All accepted papers will be published electronically and indexed in PubMed, and the best of these will be presented orally to the entire conference. PSB will also initiate submission to PubMed Central (PMC); however, PMC indexing applies only to papers that comply with the NIH Public Access Policy.
Poster abstract: Researchers wishing to present their research without official publication are encouraged to submit a one page abstract by the abstract deadline listed above to present their work in the poster sessions.
Peter Kochunov, PhD, University of Maryland School of Medicine, email@example.com
Li Shen, PhD, University of Pennsylvania, Li.Shen@pennmedicine.upenn.edu
John Darrell Van Horn, PhD, University of Virgina, firstname.lastname@example.org
Paul M. Thompson, PhD, University of South California, email@example.com
Session contact: Kathryn Hatch, University of Maryland School of Medicine, firstname.lastname@example.org
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