Life Sciences
AI-powered.
Life sciences.
Evolved.
As biological sciences strive to find answers to life’s apparent mysteries, a host of factors continues to set off newer and more complex challenges. Solving these requires powerful tools endowed with advanced data intelligence.
AI offers the rigor and versatility scientists need to interpret data from multiple perspectives. A new generation of bioinformatics solutions employs trailblazing AI and ML techniques to accelerate research programs. These groundbreaking approaches offer unmatched precision, facilitating a vast range of applications.
Next Generation Sequencing (NGS), a pivotal activity in genetic research, benefits enormously from AI.
Custom-built algorithms can enable primary analysis for quality assessment through raw sequencing, secondary analysis for identifying alignments, and tertiary analysis that provides conclusive pointers.
The efficiency and capabilities of phylogenetic analysis improve with ML-powered autoencoders. AR and VR tools offer in-depth 3D visualization of genetic and molecular structures, offering fresh insights for authoritative research. Image analysis with CNN enhances sub-cellular location and cell-segmentation studies.
With new applications being envisioned every day, AI technologies catalyze far-reaching developments in life sciences, contributing to a better future.
Domains
Drug Discovery
Prediction of the 3D structure of targeted proteins, drug interaction, toxicity, and bioactivity, drug molecule design, estimation of reaction yields, identification of therapeutic target
Clinical Trials
Design of clinical trials with NLP-based analysis of structured and unstructured data, selection of patients, target locations, and investigators, interpretation of results
Gene Therapy
Prediction of genetic changes caused by diseases, ML-powered mutation prediction, enhancement of clinical workflow efficiency, non-invasive genetic screening
Crop Improvement
Pre-germination assessment of seed quality, diagnosis of diseases with neural network and support vector techniques, crop yield modeling, analysis of plants’ interaction with the environment, Computer Vision-aided monitoring of plant health and anti-pathogen efficacy
Microbiology
AR-enhanced microscopic examination of cells, automated specimen processing with planting, streaking, gram slide preparation, broth inoculation, disk application, and sorting, interpretive algorithms for plate reading
Veterinary Sciences and Livestock Breeding
Genetic analysis of inherited diseases, parametric studies for the enhancement of the quality of meat, milk, or eggs, development of species-specific vaccines
