Sharmila Mande, Ph.D.
Principal Scientist and Head, Bio Sciences R&D, TCS Innovation Labs, Pune
Gut microbiome and health: Moving towards the new era of translational medicine
The microbes inhabiting our body outnumber our own cells by a factor of 10. The genomes of these microbes, called the ‘second genome’ are therefore expected to have great influence on our health and well being. The emerging field of metagenomics is rapidly becoming the method of choice for studying the microbial community (called microbiomes) present in various parts of the human body. Recent studies have implicated the role of gut microbiomes in several diseases and disorders. Studies have indicated gut microbiome’s role in nutrient absorption, immuno-modulation motor-response, and other key physiological processes. However, our understanding of the role of gut microbiota in malnutrition is currently incomplete. Exploration of these aspects are likely to help in understanding the microbial basis for several physiological disorders associated with malnutrition (eg, increased susceptibility to diarrhoeal pathogens) and may finally aid in devising appropriate probiotic strategies addressing this menace. A metagenomic approach was employed for analysing the differences between gut microbial communities obtained from malnourished and healthy children. Results of the analysis using TCS’ ‘Metagenomic Analysis Platform’ were discussed in detail during my talk.
Srisairam 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.