The Life Sciences, Genomics and Healthcare domain contributes a lot of scientific material related to disease, new drug combinations, genetic variants that are potential causes of rare diseases, drug development leading to modified documents in treatment regimen. Scientists and Bioinformaticians have to spend hours researching and summarizing documents or filtering genetic variants from humongous datasets (Whole Genome and Exome Sequences) to arrive at pertinent information to further their work. Semantic Web Technologies can help when augmented with intelligent Machine Learning and Natural Language Processing algorithms by extracting meaningful entities from large swathes of information and converting them into a web of linked data.
We are building a flexible and intuitive solution for Gene Variant Identification, Prioritization and Analysis as a startup incubatee within The Atal Incubation Centre- Centre for Cellular & Molecular Biology (AIC-CCMB) under the Technology Incubation and Development of Entrepreneurs (TIDE) 2.0 Scheme of Ministry of Electronics and Information Technology (MeitY), Government of India.
We welcome you to become an Early Adopter and Partner with us for this project – Sandhi Gene Variant Analysis.
As organisations grow and generate data, several data silos are created within the organization leading to independent applications across several teams. There is a need to integrate these data silos by augmenting them with contextual awareness to generate comprehensive insights without disruption and interference to existing data models. The problem of fragmented data is particularly severe in life sciences and health care companies where getting a comprehensive picture of grounds-up data from business and research functions is the top data challenge today.
Interested in building contextual aware AI/ML solutions to gather comprehensive insights from your Clinical Research, Drug Utilization and Drug Review data? Get in touch for a demo of Platform Sandhi for Life Sciences Data Integration.
Raw datasets available on the public domain and within enterprises have to undergo substantial pre-processing before it can be deployed for any analytics or insights. To compound the problem, related information is split across multiple files in different levels of granularity. For e.g, data related to states, districts, taluks, villages, towns and urban local bodies are split across multiple files and the data granularity could vary temporally (year, month, week) or units (mortality rates per 10000, sex ratio - females per 1000 etc). To perform any meaningful insights, one needs to identify relatable datasets – for e.g, which indicators are comparable across states vis-a-vis districts or at a sub district level.
Semantic Web Technologies can help by linking information from the raw data utilizing the semantics/meaning that they represent and converting it into a rich web of knowledge.
Explore our solutions customized to your needs using our Open Source semantic knowledgebase Bharathi and Platform Sandhi. Get in touch for a demo of Platform Sandhi for Public Data Applications