Author

Kajia Cao

Children's Hospital of Philadelphia

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Assay:whole-exome sequencing· 2 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Ahmad N. Abou TayouniD· 2 shared Chao WuiD· 2 shared Mahdi SarmadyiD· 2 shared Xiaonan ZhaoiD· 2 shared Avni SantaniiD· 1 shared Batsal DevkotaiD· 1 shared Bryan L. KrockiD· 1 shared Ian D. KrantziD· 1 shared

Activity by Year

2019-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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2 shared papersBioinformatics

Ahmad N. Abou TayounORCID

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2 papers

Recent·Most cited

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

Chao Wu, Xiaonan Zhao, Mark Welsh, Kellianne Costello, Kajia Cao, Ahmad N. Abou Tayoun, Marilyn M. Li, Mahdi Sarmady

The paper presents a machine learning classifier to identify true somatic variants from next-generation sequencing data, achieving high specificity and sensitivity with minimal uncertainty.

Clinical Chemistry · Oct 31, 2019Peer-reviewedBioinformatics

Rapid and Accurate Interpretation of Clinical Exomes Using Phenoxome: a Computational Phenotype-driven Approach

Chao Wu, Batsal Devkota, Perry Evans, Xiaonan Zhao, Samuel W. Baker, Rojeen Niazi, Kajia Cao, Michael Gonzalez, Pushkala Jayaraman, Laura Kathleen Conlin, Bryan L. Krock, Matthew A. Deardorff, Nancy Bettina Spinner, Ian D. Krantz, Avni Santani, , Mahdi Sarmady

Phenoxome improves clinical exome variant prioritization by integrating phenotypic and genomic data, achieving high accuracy in identifying causative variants.

European Journal of Human Genetics · Jan 9, 2019Peer-reviewedBioinformatics