Augmenting Human Health

About Us

Our innovation culture is embedded on the fact that we collaborate across different domains of biomedical research where multiple entities include universities carrying out primary research, biobanks holding biomedical results and pharmaceutical companies holding drug data. It is a complex ecosystem of various entities spread across the globe. Collaboration among these entities, including innovative partnership models, customer engagement and trust in data is of paramount importance.

At XBioSpace Research, with a diverse team on machine learning, physics and biotechnology we focus on intriguing healthcare problems like precision medicine, genomic analysis, medical process management and community medicine.

The synergy between our engineering and research teams helps us in coming up with world class product development strategies augmented with strong ability to publish research and focus on intellectual property. A team without barriers, we are committed to providing product, services and research consultancy to our global customers.


Machine Learning Techniques for MDR Bacteria

The efficient detection of multi drug resistant (MDR) bacteria from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. We are interested in the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. We compile a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. Combining PaPrBaG with existing approaches further improves prediction results.

Genomic Data Analysis for Disease Prediction

Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. We focus on improving detection, diagnosis, and therapeutic monitoring of disease. Key in the advancement has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. We are interested in several supervised and unsupervised linear methods and Bayesian inference, which have made significant impacts in the detection and diagnosis of disease in biomedicine.


Computer Vision Technology

NCP/Text processing

Decision Management

Data Engineering

Robotic Process Automation

Deep Learning Platforms



AI based Data Analysis Platform that can predict diseases based on Genome Analysis.

AI based Medical Procedure Platform for doing Drug routing and procedure management.

AI Platform that will enable critical care to healthcare units at remote locations

Get in Touch

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