Bioinformatics · Artificial Intelligence · Life Sciences
Aditya BioNova AnalyticsGen AI · Artificial Intelligence · Data Science · Bioinformatics
Aditya BioNova Analytics

Where Genomics Meets Generative AI

Gen AI–powered bioinformatics & clinical analytics, artificial intelligence and data science for life sciences.

Explore Services Discuss Your Project
Multi-omicsOncology trial analyticsMachine learning

DNA→Neural network→Data science→Generative AIIllustrative animation

Pharmaceutical

Research & analytics

Biotechnology

Translating data into insight

Healthcare

Informatics & innovation

Research

Supporting scientific progress

Expertise

Specialized Expertise Across the Bioinformatics Spectrum

View all 21 services
01

Bioinformatics & Multi-Omics

Connect genomic, transcriptomic and proteomic data with biological context.

02

Artificial Intelligence & Drug Design

Research-focused machine learning, molecular analysis and synthetic accessibility.

03

Oncology Clinical Trial Analytics

Clinical data quality, enrollment tracking, survival analysis and study dashboards.

04

Healthcare Informatics

Healthcare data integration, workflow applications and analytical dashboards.

Science in motion

Sequence → Model → Insight

Connect genomic, transcriptomic and proteomic data with biological context.

01Data in02Integrate03Explore04Quantify05Model06Insight
01 · Data inIllustrative

Genomics & variant research

Explore sequencing data with quality checks and research-focused annotation.

StrandAnnotated base pair
Fig. 1 DNA double helix. Biological data, read with computational care: a, genomic; b, transcriptomic; c, proteomic context.
02 · IntegrateIllustrative

Multi-omics integration

Bring molecular layers together around a shared research question.

GenomicTranscriptomicProteomic
Fig. 2 Each sample is followed through three molecular layers so the data can be analysed together.
03 · ExploreIllustrative

Single-cell & spatial research

Explore cellular heterogeneity with exploratory data analysis and dimensionality reduction (PCA, t‑SNE, UMAP).

Cluster 1Cluster 2Cluster 3
Fig. 3 Scattered cells resolve into clusters in a low-dimensional embedding.
04 · QuantifyIllustrative

RNA-seq & transcriptomics

Understand expression changes in the context of your experimental design.

Group AGroup BLower → higher expression
Fig. 4 Expression heatmap: rows are genes, columns are samples from two groups.
05 · ModelIllustrative

Artificial Intelligence & Drug Design

Research-focused machine learning, molecular analysis and synthetic accessibility.

Input featuresNeural networkCandidate molecule
Fig. 5 Neural network → molecule. Computational results require appropriate experimental validation.
06 · InsightIllustrative

Machine learning & pipelines

Build reproducible research models with evaluation designed in from the start.

ObservationsFitted model
Fig. 6 Observations appear, then a fitted model is drawn through them: interpretable figures, documented methods.
Illustrative animation, not project data.
Generative AI & LLMs

Generative AI, grounded in the science

Hands-on toolkit from the founder's competencies: Large Language Models, Retrieval-Augmented Generation, prompt engineering and fine-tuning, alongside domain models for biological text and sequence.

LLMsRAGLangChainLlamaIndexBioBERTDNABERTLoRA · QLoRAHugging Face Transformers
Retrieval-Augmented GenerationIllustrative
Fig. 7 Answers grounded in retrieved passages, with sources the research team can check.

Fig. 8 Gen AI assistant drafting an analysis plan, summarizing results and explaining models in plain language.

Services

Research services

Twenty-one services across omics, clinical research, data science, healthcare and publishing technology.

Healthcare technology

Healthcare Informatics & Product Design and Development

Design and develop digital healthcare products, informatics workflows and analytical applications around user needs and clearly defined requirements.

Publishing technology

Research Writing & Publication Support

Manuscript development and editorial support for PhD scholars, researchers and academic teams grounded in their own research and results.

Publishing technology

Journal Website Design & Development

Professional OJS-based academic publishing platforms for journals, scholarly societies and institutions.

Clinical research Oncology

Trial feasibility & site analytics

Assess potential recruitment pools and site readiness using approved study information.

Clinical research Oncology

Biospecimen tracking & reconciliation

Keep sample collection, shipment, receipt and testing milestones connected.

Clinical research Oncology

Safety-data summaries

Organize approved adverse-event records into review-ready summaries.

Clinical research Oncology

Patient-reported outcomes & quality of life

Explore patient experience across treatment and follow-up time points.

Clinical research Oncology

Statistical programming & CDISC support

Prepare traceable datasets, tables, listings and figures for an agreed study scope.

Laboratory support

Laboratory Testing & Research Support

Coordinate a testing plan around your research question and required analytical outputs.

Omics

Genomics & variant research

Explore sequencing data with quality checks and research-focused annotation.

Omics

RNA-seq & transcriptomics

Understand expression changes in the context of your experimental design.

Omics

Proteomics analytics

Connect protein abundance patterns with meaningful biological questions.

Omics Oncology

Multi-omics integration

Bring molecular layers together around a shared research question.

Omics

Single-cell & spatial research

Explore cellular heterogeneity and tissue-associated patterns.

Omics

Microbiome & metagenomics

Investigate community composition and functional research questions.

Clinical research Oncology

Oncology & survival analysis

Translate follow-up data into an interpretable time-to-event analysis.

Clinical research Oncology

Clinical trial biostatistics

Define the statistical question before choosing the model.

Clinical research Oncology

Clinical data quality

Find missing values, inconsistent records and unresolved data questions.

Clinical research Oncology

Trial operations dashboards

Give study teams a clear view of enrollment and follow-up activity.

Clinical research Oncology

Biomarker research

Evaluate candidate markers with transparent validation and limitations.

Data science

Machine learning & pipelines

Build reproducible research models with evaluation designed in from the start.

Research & Publishing Support

Additional services to support your research journey.

  • Journal Website Development
  • Scientific Writing & Editing
  • Laboratory Research Support
Oncology

Connected insight. Across your oncology study.

From feasibility and follow-up to biomarkers and statistical reporting, choose support around your study's actual needs.

01

Plan & track

Site feasibility, enrollment and biospecimen visibility.

02

Review & resolve

Clinical data checks, safety summaries and query review.

03

Analyze & explain

Survival endpoints, molecular associations and patient-reported outcomes.

04

Program & report

Agreed analysis datasets, tables, listings and figures.

Analyze & explainIllustrative

Oncology & survival analysis

Translate follow-up data into an interpretable time-to-event analysis.

Group AGroup BCensored
Fig. 9 Kaplan–Meier survival curves (illustrative): step-downs mark observed events, ticks mark censored follow-up.
Plan & trackIllustrative

Trial operations dashboards

Give study teams a clear view of enrollment and follow-up activity.

Cumulative enrollmentPlan
Fig. 10 Study dashboard (illustrative): enrollment, follow-up and query activity in one view.
Oncology-ready services
Trial feasibility & site analyticsBiospecimen tracking & reconciliationSafety-data summariesPatient-reported outcomes & quality of lifeStatistical programming & CDISC supportMulti-omics integrationOncology & survival analysisClinical trial biostatisticsClinical data qualityTrial operations dashboardsBiomarker research
Explore oncology support
How we work

Our approach

STEP 01

Define the question

Agree on the objective, study design, data requirements and scope.

STEP 02

Review the data

Assess quality, permissions, missing values and potential sources of bias.

STEP 03

Analyze & validate

Apply an agreed workflow with checks, documented methods and suitable evaluation.

STEP 04

Explain & deliver

Provide interpretable figures, reports and the agreed reproducible analysis materials.

Research & publishing

Make the methods as clear as the results.

Public-data demonstration: Breast cancer survival analytics.

  • GBSG2 cohort: 686 patients and 299 observed events.
  • A fixed 75/25 split compared Cox regression (C-index 0.7045) and a random survival forest (0.7063).
  • Interpretable figures, documented methods and stated limitations.
Public-data demonstration
Breast cancer survival analytics
Research demonstration
686Patients
299Observed events
0.7045C-index · Cox regression
0.7063C-index · Random survival forest
Cohort composition GBSG2 · one square = one patient
Observed events (299)Patients (686)
Concordance index 0.5 = chance · 1.0 = perfect ranking
Cox 0.7045 RSF 0.7063 0.51.00.90.8
Single-split demonstration; no external validation or treatment-effect claim.
Project Concepts · Available for Development

Explore Artificial Intelligence, Drug Design & Trial Management

Artificial Intelligence & Machine Learning for Biological Data

Model development for classification, clustering and research prediction using curated biological datasets.

Computational Drug Design

Artificial Intelligence methods for research workflows in molecular screening, synthetic accessibility and retrosynthesis analysis.

Agreed molecular datasets, model assessment, candidate prioritization and documented limitations. Computational results require appropriate experimental validation.

Oncology Clinical Trial Management

Enrollment and visit tracking, site milestones, biospecimen status, query workflows and operational dashboards.

These concepts describe potential projects, not completed client implementations or validated clinical products.
Explore project scope

Our founder's postdoctoral experience in South Korea and China informs an international research perspective.

Connect globally · Advance research together

Global Scientific Collaboration & Research Partnerships

Backed by a highly qualified team and strong international research collaborations, Aditya BioNova Analytics brings together expertise in bioinformatics, Artificial Intelligence, data science and life sciences.

Aditya BioNova Analytics welcomes cross-border research opportunities that connect biological expertise, computational methods and clearly defined scientific goals.

Collaborative research

Explore joint bioinformatics, multi-omics, computational drug-design and oncology research projects with agreed scientific roles.

Dedicated Research & Analytics Support

Extend your team with scoped data analysis, model development, scientific editing or publishing-platform support through project-based or ongoing engagements.

Methods & knowledge exchange

Work together on reproducible workflows, scientific data curation, research interpretation and technical knowledge sharing.

About

Scientific thinking. Practical collaboration.

Aditya BioNova Analytics is a research analytics venture built around bioinformatics and scientific data science.

We work with research groups, laboratories, biotechnology teams and clinical research organizations to define practical analyses and turn complex datasets into clear, reproducible deliverables.

Dr. Sambhaji Balaso Thakar
FounderAditya BioNova Analytics
Founder · Scientific Leadership

Dr. Sambhaji Balaso Thakar

Senior Bioinformatician · Data Scientist
MSc BioinformaticsPh.D. BiotechnologyPost-Doctoral Fellow, CAU, Seoul, South KoreaPost-Doctoral Fellow, Anhui Agricultural University (AAU), Hefei, China
Post-Doctoral Research

Chung-Ang University (CAU), Seoul, South Korea · Anhui Agricultural University (AAU), Hefei, China

Education

MSc Bioinformatics, S.R.T.M. University, Nanded · PhD Biotechnology (Structural Bioinformatics), Shivaji University, Kolhapur

At Aditya BioNova Analytics, he leads scientific project scoping and review, with an emphasis on transparent methods and reproducible analysis.

Meet the Founder Research Publications Database Projects
FAQ

Before we begin

Share your research objective and the data you have.

Start a project
What should I share for an initial proposal?

Share your research objective, the type of data you have, approximate sample count and your timeline. Please share project details only. Do not include patient identifiers, credentials or confidential raw datasets.

Can you work with our existing data?

Yes. We first review data quality, permissions, missing values and potential sources of bias, then agree the inputs required for the analysis.

How are projects priced?

Fees depend on the agreed scope, data readiness, computing and validation needs. Options include scoped pilot projects, complete research analyses and monthly analytics support.

Will I receive the code and methods?

Deliverables include interpretable figures, reports and the agreed reproducible analysis materials, with documented methods.

Does the website automatically analyze patient data?

No. The enquiry form does not run scientific analyses. Data transfer arrangements and permissions are agreed separately before a research engagement.

Start a project

Scientific expertise. Clear insights. Meaningful research.

Share your research objective, the type of data you have, approximate sample count and your timeline.

Request a proposal Explore Services
Start a project

Let's discuss your next research project.

Share your research objective and the data you have.

  • Define the question — objective, study design, data requirements and scope.
  • Review the data — quality, permissions, missing values and potential sources of bias.
  • Explain & deliver — interpretable figures, reports and agreed reproducible materials.
Please share project details only. Do not include patient identifiers, credentials or confidential raw datasets.

Tell us about your next study.

Share your research objective and the data you have.