Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment

Anand Ramachandran, Huiren Li, Eric W Klee, Steven S. Lumetta, Deming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-throughput techniques have revolutionized the study of genomics and molecular biology in recent years. These methods provide a large quantity of sequence data, and have applications in different areas of bioinformatics. One can sequence parts or whole of an organism's DNA to determine genetic information about an individual or a population, measure expression levels of different genes under different conditions, and determine binding affinity of proteins to DNA segments revealing details regarding gene regulation, at a higher resolution than before. However, different high-throughput methods that target even a single application have different underlying error models. Robust analytic pipelines are necessary to extract necessary information from the raw data. In this paper, we discuss future research directions for developing such analytics using techniques from Machine Learning and Deep Neural Networks. We focus on two applications that will affect the diagnosis and treatment of cancer.

Original languageEnglish (US)
Title of host publicationASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-21
Number of pages6
Volume2018-January
ISBN (Electronic)9781509006021
DOIs
StatePublished - Feb 20 2018
Event23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of
Duration: Jan 22 2018Jan 25 2018

Other

Other23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018
CountryKorea, Republic of
CityJeju
Period1/22/181/25/18

Fingerprint

DNA
Throughput
Molecular biology
Bioinformatics
Gene expression
Learning systems
Pipelines
Genes
Proteins
Deep learning
Deep neural networks
Genomics

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Cite this

Ramachandran, A., Li, H., Klee, E. W., Lumetta, S. S., & Chen, D. (2018). Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings (Vol. 2018-January, pp. 16-21). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASPDAC.2018.8297276

Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment. / Ramachandran, Anand; Li, Huiren; Klee, Eric W; Lumetta, Steven S.; Chen, Deming.

ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 16-21.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ramachandran, A, Li, H, Klee, EW, Lumetta, SS & Chen, D 2018, Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment. in ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 16-21, 23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018, Jeju, Korea, Republic of, 1/22/18. https://doi.org/10.1109/ASPDAC.2018.8297276
Ramachandran A, Li H, Klee EW, Lumetta SS, Chen D. Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 16-21 https://doi.org/10.1109/ASPDAC.2018.8297276
Ramachandran, Anand ; Li, Huiren ; Klee, Eric W ; Lumetta, Steven S. ; Chen, Deming. / Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment. ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 16-21
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