Expand your horizons of OMICs data analysis and visualization
Sophisticated artificial intelligence algorithms suggest viable hypotheses of novel drug targets, reducing required time from several months to the span of just a few clicks
Zero bioinformatics experience is required
A roadmap to successful disease research and target identification strategies requires considerable resources spent on search, collection and interpretation of data during several months of work.
PandaOmics provides a unique opportunity to both explore the unknown of OMICs data and interpret it in the context of all the scientific data generated by the scientific community.
Transformer-based knowledge graph generator:
a deep learning model that reads scientific publications and automatically generates a graphical representation of their content

Cross-dataset comparison and data harmonization:
the ability to harmonize and combine several datasets into one, robust harmonization quality control

Improved Meta-analysis and Disease pages:
omics data statistics preview for disease page, improved expression heatmap rendering and image downloads across the application

Content updates: improved named entity recognition methodology, frequent updates of text and clinical trials data

100+ manually curated Disease pages:
pre-calculated Target ID and Compound ID for most if not all the diseases from the pipelines of the top 20 pharma companies and many more with 1000+ high-quality datasets added
PandaOmics 3.0 Fleming
Main Features
PandaOmics for Research
Access the full set of OMICs data generated
by the scientific community so far. You do not need
to spend your time trying to convert your data into
an interpretable format or wait for a bioinformatician
to do that for you — instead, you will find all
the data already processed and uploaded in a uniform way, so you can focus on science and data interpretation.
OMICs data analysis
Pathway analysis is a crucial step toward a complete understanding of how data works. It converts a list of seemingly unrelated genes into a connected story based on dysregulated molecular processes. PandaOmics uses a proprietary pathway analysis approach called iPanda to infer pathway activation or inhibition. Results published in Nature communications in 2016 demonstrated the algorithm outperforming other pathway analysis tools.
Comprehensive pathway analysis
Once you have brought the context of related OMICs to your disease, start exploring the set of actionable drug target hypotheses that PandaOmics provides. A set of artificial intelligence algorithms analyzes the whole pool of text data, mentioning all significant genes from your experiment
in the context of the disease you are studying, and brings
both clear and hidden connections from the dataset
and publications, grants, patents, clinical trials, etc.
Target Identification
  • 5M OMICs data samples
    A full spectrum of transcriptomics, genomics, epigenomics, proteomics, single cell data
    generated by the scientific community
  • 1.3M Compound & Biologics
    Drugs from Phase 1 of clinical trial
    till Launched phase
  • 3M Grants
    Life sciences research grant funding
  • 3.8M Patents
    Patents covering the life sciences industry
  • 342K Clinical Trials
    Explore extra knowledge related
    to the clinical trials design
  • 30M Publications
    Published biomedical research results
PandaOmics for Target ID
PandaOmics allows target exploration even if you don't have any OMICs data. Starting from a disease of interest, the system applies an artificial intelligence hypothesis generation system to rank all related genes, taking into account all OMICs datasets stored in the system as well any connections from publications, grants, patents, clinical trials, etc.
Target Identification
Once you have a short list of putative targets, PandaOmics will help you to evaluate all the background evidence connecting them to the disease of interest.

Discover both molecular and text evidence connecting
a target to a disease
Target Evaluation
Artificial intelligence algorithms predict the chances
of a potential target to enter Phase 1 of clinical trials
for any disease in the next five years, and estimate
the chance of a successful phase-to-phase transition
for disease-specific trials
Clinical Trials
transition prediction
Estimate the growth of attention to the gene given during the last five-year period (both disease-agnostic and disease-specific) the artificial intelligence technology predicts the burst of scientific community attention to a gene in the next three years
Target attention prediction
Explore an uncharted chemical space for perfect-fit compounds. Operate beyond all existing screening libraries and skip the effort of a perfect scaffold search and optimization.

Chemistry42 is a fully automated artificial intelligence platform that does everything for you in a week
Generate Novel compounds
Target ID
Case study
Would you like to know more?
We have had discussions with several AI startups and Insilico Medicine is the only company which provides excellent service not only with collaboration but also with software license. PandaOmics enabled us to analyze Microarray or RNAseq data easily and quickly. We are satisfied with its usability and the results generated from Insilico's software. We think PandaOmics is a worthwhile software
for target identification. We also greatly appreciate their kind support. They are professionals,
and always answer our questions quickly, precisely and kindly.

As we work to accelerate our understanding of diabetes and other diseases, PandaOmics is just the resource we sought to help us quickly access, analyze, visualize and interpret externally available data effectively to drive our research efforts. This new platform empowers our scientists to expedite biomarker identification, repurpose existing therapeutics and discover new drugs.

Dr. Daniel Robertson
Vice President of Digital Technology at the Indiana Biosciences Research Institute
21 March, 2023
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