PhGC: A Machine Learning Based Workﬂow for Phenotype-Genotype Co-analysis on Autism

10 pages•Published: March 11, 2020

Safa Shubbar, Chen Fu, Zhi Liu, Anthony Wynshaw-Boris and Qiang Guan

Abstract

Autism spectrum disorder (ASD) is a heterogeneous disorder, diagnostic tools attempt to identify homogeneous subtypes within ASD. Previous studies found many behavioral/- physiological commodities for ASD, but the clear association between commodities and underlying genetic mechanisms remains unknown. In this paper, we want to leverage ma- chine learning to figure out the relationship between genotype and phenotype in ASD. To this purpose, we propose PhGC pipeline to leverage machine learning approach to to identify behavioral phenotypes of ASD based on their corresponding genomics data. We utilize unsupervised clustering algorithms to extract the core members of each clusters and profile the core member subsets to explore the characteristics using genotype data from the same dataset. Our genome annotation results showed that most of the alleles with different frequency among clusters were represented by the core members.

Keyphrases: autism, cluster analysis, genotype, machine learning, phenotype, phgc

In: Qin Ding, Oliver Eulenstein and Hisham Al-Mubaid (editors). Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, vol 70, pages 49-58.

Links:	https://easychair.org/publications/paper/mJFF
	https://doi.org/10.29007/ctfl

BibTeX entry

@inproceedings{BICOB2020:PhGC_Machine_Learning_Based,
  author    = {Safa Shubbar and Chen Fu and Zhi Liu and Anthony Wynshaw-Boris and Qiang Guan},
  title     = {PhGC: A Machine Learning Based Workﬂow for Phenotype-Genotype Co-analysis on Autism},
  booktitle = {Proceedings of the 12th International Conference on Bioinformatics and Computational Biology},
  editor    = {Qin Ding and Oliver Eulenstein and Hisham Al-Mubaid},
  series    = {EPiC Series in Computing},
  volume    = {70},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/mJFF},
  doi       = {10.29007/ctfl},
  pages     = {49-58},
  year      = {2020}}

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