Codes, not identities
Patient identifiers are stripped before anything moves. Internal codes are enough for the analysis; only minimal metadata is required.
Ayurnidaan brings structured classical knowledge and clinical phenotyping. AI Ops Pros brings pipelines, compute and a research scholar network. Together we test which traditional principles produce reproducible biological signal — starting with public data only.
Modern precision medicine needs frameworks that group individuals by biologically meaningful phenotypes — and reactive care only acts once disease has already manifested.
Ayurveda operates a centuries-old stratification system built for exactly that purpose: Prakriti as a stable baseline, Vikriti as deviation from it.
Do these frameworks contain reproducible biological signals that can be validated with modern genomics and machine learning?
Cardio-metabolic disease and lifestyle disorders, intercepted at the stratification and prevention stage: understand the individual, identify biological risk, personalize the intervention, monitor longitudinally.
The line between the two is the whole point of the collaboration. Everything on the left has peer-reviewed support; everything on the right is what we would be testing.
India's CSIR-IGIB / TRISUTRA consortium has led 15+ years of peer-reviewed Ayurgenomics research. Healthy individuals of contrasting extreme Prakriti types show statistically significant differences in biochemical, haematological and gene-expression parameters, including EGLN1 variation linked to high-altitude adaptation.
Machine learning recovers Prakriti categories from structured phenotypic traits alone (Tiwari et al., PLOS ONE, 2017).
Multiple independent meta-analyses across 100+ randomized trials report that curcumin significantly reduces fasting blood glucose, HbA1c and LDL in type 2 diabetes. Clinical trials (Biswal et al., 2013) report reduced chemotherapy-induced fatigue with Ashwagandha in breast cancer patients.
The infrastructure exists: IMPPAT, a curated public database of roughly 4,000 Indian medicinal plants and 18,000 structure-annotated phytochemicals, built for network-pharmacology screening.
Can scalable phenotypic data map consistently onto reproducible biomarker and clinical signatures?
Does Prakriti-stratified lifestyle intervention, with specific botanical adjuncts, modulate validated inflammatory and arterial-stiffness markers?
Does longitudinal Vikriti tracking correlate with measurable physiological change — heart-rate variability, continuous glucose?
Can multi-target mapping of fatigue and HPA-axis pathways using IMPPAT phytochemicals generate candidate mechanisms worth testing? Herb–drug interaction screening is a prerequisite, not an afterthought.
The execution gap is the honest part: computational predictions generate hypotheses. They still require experimental and clinical validation.
Ayurnidaan gains robust ML infrastructure and pipeline execution for a domain that has had little of either.
AI Ops Pros gains access to a novel, source-traceable research pipeline and a scientifically serious problem to work on.
Can we computationally screen one specific classical cardio-metabolic formulation against known CMD gene networks? Public data, weeks not quarters, and a result that tests the working relationship before anything scales.
One classical cardio-metabolic formulation, expanded into its constituent phytochemicals through IMPPAT 2.0.
SwissTargetPrediction for candidate targets, STRING for protein interaction network construction, Cytoscape for hub-gene analysis.
A ranked list of candidate molecular targets and pathways, benchmarked against known CMD gene networks — publishable and IP-eligible.
The rules. Public datasets only — IMPPAT, GEO, STRING. No proprietary patient data exchanged. No IP entanglement at this stage.
The cost. The target is an analysis that runs for a few dollars rather than a few thousand, so cost stops being the reason a question goes untested.
The honest limit. A ranked target list is a hypothesis generator. Experimental and clinical validation still decides what is true.
Health data carries obligations. The working arrangement is designed so that those obligations shape the pipeline rather than being bolted on afterwards.
Patient identifiers are stripped before anything moves. Internal codes are enough for the analysis; only minimal metadata is required.
If data must stay within India, India-based scholars run the analysis and the US-based leads restrict themselves to high-level review.
Germline and somatic variants stay largely fixed, so before-and-after studies gain more from RNA sequencing — or methylation — where the signal is dynamic. DNA, RNA and bisulfite pipelines are all supported.
Illumina BaseSpace exports or FASTQ both work. A standard operating procedure for formats and transfer is provided before the first handoff.
Ayurveda is never positioned as a cancer cure or as a replacement for standard oncology regimens. Supportive-care claims stay inside what the trial evidence supports.
No fees during this phase. What we ask in return is a testimonial, joint publications, and introductions to your network once the work has proven itself.
Scholar résumés and LinkedIn profiles shared by Drive. Ayurnidaan sends a brief description of the data on hand. The agreement moves in parallel.
Scholars are briefed on the domain and the workflow is architected, so analysis can begin the day the agreement is signed.
The formulation screen runs end to end on public data, with the method documented as it goes.
Ranked targets reviewed jointly, a write-up drafted for publication, and a decision on whether to scale to patient-data work.
BAMS. Bridges classical literature and clinical preventive care.
24+ years in academia and research, translating complex systems into computational models.
PhD. Genomics and transcriptomics analysis; architects the workflows and reviews every result.
~20 years in software. Builds the pipelines, the infrastructure and the Sutra Loop engine behind the team.
Send a short description of the data you have on hand, and we will have the team prepared before the paperwork is finished.