Machine Learning Solutions to Translational Genomics
SEQOME-DL: Artificial Intelligence Deep Learning Platform for Development of Prognostic Model
SEQOME-DL is a Deep Learning Framework for Developing Predictive models. The platform has been successfully applied to Genomics, Healthcare, Drug Discovery and has potential to be used for a wide range of applications. It uses state of art technologies for Feature identification, Optimization of learning parameters, Network pruning and Cross validation. The framework is equipped with APIs to seamlessly integrate with existing IT infrastructure, Health Informatics systems, Diagnostics Lab and a wide range of existing systems.
SEQOME Workbench is our Cloud based Big Data Bioinformatics Analytic platform optimised for Next Generation Sequencing and Simulations.
We have developed State of Art Cancer Specific Networks to support our Translational Genomics Pipeline
Our Network maps > 14,000 Genes mapped to > 40 Cancer Specific Gene Function. Our Drug-Gene Network includes 250 Cancer Drugs and 474 Genes and associated Pathways. This integrates with our High Throughput Translational Genomics pipeline and provides enhanced analytics and insight to Biological Systems. Our network driven approach is designed to help scientist and clinical research companies better interpretability of the Omics data generated from Next Generation Sequencing, Microarrays and Mass Spectrometry.
Learn from the Experts: We organize Biostatistics and Bioinformatics workshop for Lab Scientist
From our BLOGS
What all to look for in your RNA Sequencing Data
RNA Sequencing is a treasure-chest of information and quiet often we miss on potential ground breaking information in the RNA-SEQ datasets. This article will focus on conventional applications of RNA Sequencing, and will explore mining information for cSNP, Insertions Deletions & Fusion Genes, Alternate Splicing, Novel Genes/Exon, eQTL, and more. Read the BLOG
Working with Multi-Omics Dataset
Lab Scientist can easily be drained out interpreting results from a single set of OMICS data. Imagine the complexity working with Multiple and Varied OMICS experiment. This article focuses on methods on working with multi-omics data-set. Read the BLOG