Author

Beate Peter

Arizona State UniversityORCID 0000-0002-9083-8522Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:whole-exome sequencing· 3 Assay:metabolomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1

Frequent Co-Authors

Valentin DinuiD· 3 shared Haiwei GuiD· 2 shared Rohit NandakumariD· 2 shared Wendy H. RaskindiD· 2 shared Xiaojian ShiiD· 2 shared Yookyung KimiD· 2 shared Akshit ModiiD· 1 shared Caitlin V. RaaziD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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3 shared papersBioinformatics

Valentin DinuORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Developmental Convergence Across Diverse Genetic Architectures in Childhood Apraxia of Speech

Tiffanie White, Marjan Chapi, Caitlin V. Raaz, Laurel Bruce, Akshit Modi, Valentin Dinu, Li Liu, Judith Klein‐Seetharaman, Beate Peter

The paper identifies multiple genetic variants associated with childhood apraxia of speech through whole-exome sequencing and functional analysis, proposing four novel candidate genes.

bioRxiv · Oct 4, 2026 · PreprintGenetics

Joint exome and metabolome analysis in individuals with dyslexia: Evidence for associated dysregulations of olfactory perception and autoimmune functions

Rohit Nandakumar, Xiaojian Shi, Haiwei Gu, Yookyung Kim, Wendy H. Raskind, Beate Peter, Valentin Dinu

This study analyzes exome and metabolome data from individuals with dyslexia to identify associated dysregulations in olfactory perception and immune functions.

bioRxiv · Jun 27, 2024 · PreprintBioinformatics

A data-fusion approach to identifying developmental dyslexia from multi-omics datasets

Jackson Carrion, Rohit Nandakumar, Xiaojian Shi, Haiwei Gu, Yookyung Kim, Wendy H. Raskind, Beate Peter, Valentin Dinu

This study uses data fusion and machine learning to analyze multi-omics and clinical data, finding that ensemble methods outperform traditional techniques in identifying developmental dyslexia with high accuracy.

bioRxiv · Feb 27, 2023 · PreprintBioinformatics