Rajaraman Kanagasabai
Overview
Explore the profile of Rajaraman Kanagasabai including associated specialties, affiliations and a list of published articles.
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Articles
6
Citations
50
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0
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Recent Articles
1.
Narasimhan K, Govindasamy M, Gauthaman K, Kamal M, Abuzenadeh A, Al-Qahtani M, et al.
CNS Neurol Disord Drug Targets
. 2013 Sep;
13(3):408-17.
PMID: 24059306
Diabetes mellitus (DM) is characterized by hyperglycemia either due to deficient insulin production (Type 1 Diabetes mellitus) or peripheral insulin resistance of the cells (Type 2 Diabetes mellitus). Both Type...
2.
Kanagasabai R, Narasimhan K, Low H, Ang W, Fernandis A, Wenk M, et al.
Summit Transl Bioinform
. 2011 Feb;
2009:61-5.
PMID: 21347172
The role of lipids in cancer during the genesis, progression and subsequent metastasis stages is increasingly discussed in the scientific literature. This information is discussed in a wide range of...
3.
Wijaya E, Yiu S, Son N, Kanagasabai R, Sung W
Bioinformatics
. 2008 Aug;
24(20):2288-95.
PMID: 18697768
Motivation: Locating transcription factor binding sites (motifs) is a key step in understanding gene regulation. Based on Tompa's benchmark study, the performance of current de novo motif finders is far...
4.
Rajapakse M, Kanagasabai R, Ang W, Veeramani A, Schreiber M, Baker C
J Biomed Inform
. 2008 Jun;
41(5):806-15.
PMID: 18524685
Uninhibited access to the unstructured information distributed across the web and in scientific literature databases continues to be beyond the reach of scientists and health professionals. To address this challenge...
5.
Baker C, Kanagasabai R, Ang W, Veeramani A, Low H, Wenk M
BMC Bioinformatics
. 2008 Mar;
9 Suppl 1:S5.
PMID: 18315858
Background: The indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge...
6.
Kanagasabai R, Choo K, Ranganathan S, Baker C
J Bioinform Comput Biol
. 2008 Jan;
5(6):1319-37.
PMID: 18172931
Rich information on point mutation studies is scattered across heterogeneous data sources. This paper presents an automated workflow for mining mutation annotations from full-text biomedical literature using natural language processing...