Aug
12
Mon
2013
Invited Talk: Discovery, engineering and applications of Blue Fish Protein with Red Flourescence @ Sathyam Hall
Aug 12 @ 10:00 am – 10:15 am

RamaswamyS. Ramaswamy, Ph.D.
CEO of c-CAMP, Dean, inStem, NCBS, Bangalore, India


Discovery, engineering and applications of Blue Fish Protein with Red Fluorescence

Swagatha Ghosh, Chi-Li Yu, Daniel Ferraro,  Sai Sudha, Wayne Schaefer, David T Gibson and S. Ramaswamy

Fluorescent proteins and their applications have revolutionized our understanding of biology significantly.  In spite of several years since the discovery of the classic GFP, proteins of this class are used as the standard flag bearers.  We have recently discovered a protein from the fish Sanders vitrius that shows interesting fluorescent properties – including a 280 nm stoke shift and infrared emission.  The crystal structure of the wild type protein shows that it is a tetramer.  We have engineered mutations to make a monomer with very similar fluorescent properties. We have used this protein for tissue imaging as well as for in cell-fluorescence successfully

Ramaswamy (1) Ramaswamy (2) Ramaswamy (3) Ramaswamy (4)

Aug
13
Tue
2013
Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.