From our empirical data associations and data analysis, we generate data objects which we then classify as "known" or "unknown" in scientific literature. The underlying data objects that generate hypotheses follow the same methodology and process as those that are validated by literature to populate our gene summaries and topic overviews. Over time, our hypotheses are tested through experimentation and migrate to 'known' categories once proven.
How can I know if hypotheses are credible?
Articles in this category
- What features and capabilities does Heureka support?
- What sets Heureka apart from other AI solutions?
- Do I need a computational background to use Heureka tools?
- Does Heureka really analyze each project from scratch, or does it utilize templates or pipelines to generate reports?
- What is a QuartzReport?
- How does Heureka's bioinformatics and interpretation solution, QuartzReport, work?
- How does Heureka come up with hypotheses?
- Could a "hypothesis" be known or a "gene summary fact" be unsubstantiated by scientific literature?
- How should I cite Heureka in my work?
- How do credits work?
- What is 'Extended Reasoning' in ARC?