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 language | English (US) |
|---|---|
| Title of host publication | ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 16-21 |
| Number of pages | 6 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781509006021 |
| DOIs | |
| State | Published - Feb 20 2018 |
| Event | 23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of Duration: Jan 22 2018 → Jan 25 2018 |
Other
| Other | 23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju |
| Period | 1/22/18 → 1/25/18 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Deep Learning for Better Variant Calling for Cancer Diagnosis and Treatment
AU - Ramachandran, Anand
AU - Li, Huiren
AU - Klee, Eric W
AU - Lumetta, Steven S.
AU - Chen, Deming
PY - 2018/2/20
Y1 - 2018/2/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85045312654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045312654&partnerID=8YFLogxK
U2 - 10.1109/ASPDAC.2018.8297276
DO - 10.1109/ASPDAC.2018.8297276
M3 - Conference contribution
AN - SCOPUS:85045312654
VL - 2018-January
SP - 16
EP - 21
BT - ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -