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Case Studies & Applied Research
Peer-reviewed research and applied biotechnology projects demonstrating empirical rigor, computational precision, and measurable outcomes.
Systematic multi-omics profiling combining whole-exome sequencing with RNA-Seq to uncover novel metabolic vulnerabilities in KRAS-driven pancreatic cancer cell lines.
Integrated differential expression with flux balance analysis (FBA) and deep neural network affinity screening against secondary metabolite libraries.
Identified 3 novel small-molecule candidate binders showing >65% cell viability reduction in vitro with confirmed micromolar binding affinity.
Graph neural network screening engine evaluating over 2.4 million small molecules against oncogenic kinase targets to identify selective, non-covalent inhibitors.
Multi-task geometric neural networks evaluating docking poses, ADMET pharmacokinetics, and off-target cytotoxicity simultaneously against PDB structural pockets.
Accelerated hit-to-lead discovery cycles by 40% with nanomolar binding validation in wet-lab assays.
Connected agricultural biosensor grid analyzing soil microbial respiration, moisture tension, and nutrient availability across commercial acreage in Karnataka.
Edge computing nodes transmitting hourly telemetry via LoRaWAN to predictive models scheduling autonomous variable-rate drip irrigation.
Demonstrated 25% increase in crop yield alongside a 30% reduction in water and nitrogen fertilizer runoff over two growing seasons.
Consolidated genomic atlas integrating single-cell RNA-Seq and spatial transcriptomics to uncover diagnostic biomarkers for rare neuro-metabolic disorders.
Unsupervised manifold learning and cell-type deconvolution models mapped against patient clinical phenotyping cohorts.
Established an open-access reference atlas characterizing 42 previously unannotated splice variants correlated with disease progression.
We collaborate with academic groups and commercial biopharma under strict confidentiality and structured project agreements.
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