Abstract
Current clinical methods base disease detection and identification heavily on the description of symptoms by the patient. This leads to inaccuracy because of the errors that may arise in the quantification of the symptoms and also does not give a complete idea about the presence of any particular disease. The prediction of cellular diseases is still more challenging; for we have no measure on the exact quantity, quality and extremeness. The typical symptoms for these diseases are visible at a later stage allowing the disease to silently progress. This paper provides an efficient and novel way of detection and identification of pancreatitis and breast cancer using a combination of sequence data and information retrieval algorithms to provide the most accurate result. The developed system maintains a knowledge base of the mutations of the diseases causing breast cancer and pancreatitis and thus uses techniques of protein sequence scoring and information retrieval for providing the best match of patient protein sequence with the mutations stored. The system has been tested with mutations available online and gives 98 % accurate results.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015 |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 565-572 |
| Number of pages | 8 |
| Volume | 43 |
| ISBN (Print) | 9788132225379 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015 - Bhubaneshwar, India Duration: 23-06-2015 → 25-06-2015 |
Publication series
| Name | Smart Innovation, Systems and Technologies |
|---|---|
| Volume | 43 |
| ISSN (Print) | 2190-3018 |
| ISSN (Electronic) | 2190-3026 |
Conference
| Conference | 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015 |
|---|---|
| Country | India |
| City | Bhubaneshwar |
| Period | 23-06-15 → 25-06-15 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Computer Science(all)
Cite this
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Disease detection and identification using sequence data and information retrieval methods. / Joshi, Sankranti; Radhika, Pai M.; Manohara, Pai M.M.
Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015. Vol. 43 Springer Science and Business Media Deutschland GmbH, 2016. p. 565-572 (Smart Innovation, Systems and Technologies; Vol. 43).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Disease detection and identification using sequence data and information retrieval methods
AU - Joshi, Sankranti
AU - Radhika, Pai M.
AU - Manohara, Pai M.M.
PY - 2016
Y1 - 2016
N2 - Current clinical methods base disease detection and identification heavily on the description of symptoms by the patient. This leads to inaccuracy because of the errors that may arise in the quantification of the symptoms and also does not give a complete idea about the presence of any particular disease. The prediction of cellular diseases is still more challenging; for we have no measure on the exact quantity, quality and extremeness. The typical symptoms for these diseases are visible at a later stage allowing the disease to silently progress. This paper provides an efficient and novel way of detection and identification of pancreatitis and breast cancer using a combination of sequence data and information retrieval algorithms to provide the most accurate result. The developed system maintains a knowledge base of the mutations of the diseases causing breast cancer and pancreatitis and thus uses techniques of protein sequence scoring and information retrieval for providing the best match of patient protein sequence with the mutations stored. The system has been tested with mutations available online and gives 98 % accurate results.
AB - Current clinical methods base disease detection and identification heavily on the description of symptoms by the patient. This leads to inaccuracy because of the errors that may arise in the quantification of the symptoms and also does not give a complete idea about the presence of any particular disease. The prediction of cellular diseases is still more challenging; for we have no measure on the exact quantity, quality and extremeness. The typical symptoms for these diseases are visible at a later stage allowing the disease to silently progress. This paper provides an efficient and novel way of detection and identification of pancreatitis and breast cancer using a combination of sequence data and information retrieval algorithms to provide the most accurate result. The developed system maintains a knowledge base of the mutations of the diseases causing breast cancer and pancreatitis and thus uses techniques of protein sequence scoring and information retrieval for providing the best match of patient protein sequence with the mutations stored. The system has been tested with mutations available online and gives 98 % accurate results.
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UR - http://www.scopus.com/inward/citedby.url?scp=84951753110&partnerID=8YFLogxK
U2 - 10.1007/978-81-322-2538-6_58
DO - 10.1007/978-81-322-2538-6_58
M3 - Conference contribution
SN - 9788132225379
VL - 43
T3 - Smart Innovation, Systems and Technologies
SP - 565
EP - 572
BT - Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015
PB - Springer Science and Business Media Deutschland GmbH
ER -