Multimodal Genotype and Phenotype Data Integration to Improve Partial Data-Based Longitudinal Prediction-Reference-Cited by-同舟云学术

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Multimodal Genotype and Phenotype Data Integration to Improve Partial Data-Based Longitudinal Prediction

Published:2022-12-01 Issue:12 Volume:29 Page:1324-1345
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Ganjdanesh Alireza¹,Zhang Jipeng²,Yan Sarah³,Chen Wei²⁴⁵^ORCID,Huang Heng¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

2. Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

3. West Windsor-Plainsboro High School South, Princeton Junction, New Jersey, USA.

4. Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.

5. Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modeling and Simulation

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2022.0378

Reference59 articles.

1. Aakur SN, Narayanan S, Indla, et al. Mg-net: Leveraging pseudo-imaging for multi-modal metagenome analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2021; pp. 592–602.

2. The age-related eye disease study system for classifying age-related macular degeneration from stereoscopic color fundus photographs: the age-related eye disease study report number 6

3. Age-related macular degeneration

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