We can cure cancer by preventing it.
Diagnostics based on cell-free DNA can be improved to the point that they both (1) catch all cancers at their earliest stage and (2) are affordable enough to be ubiquitously available and accessible.
The next generation of cell free diagnostics
Edge PCR plus broad demand may drive down the price of comprehensive testing.
Better cfDNA biomarker sets
Biomarker sets can be improved progressively to include more markers that indicate disease state and/or treatment relevance.
Edge PCR
Low-cost PCR at the edge by consumers will permit bulk testing of PCR-based biomarker sets as well as pre-processing for array-based assays.
Proteome and cfDNA arrays
Detection density can be scaled substantially using array technology both for cfDNA and whole proteome detection.
Cellphone imaging
Cellphone can be used to image assays and test strips permitting easy and low cost detection at the edge.
Whether clinic or DTC, liquid cancer screening is becoming ubiquitous
DNA-based liquid biopsy screening is already becoming ubiquitous, now available via neighborhood clinics and in some cases at $0 of insurance cost. Research can add to this and/or facilitate direct-to-consumer testing along the lines of glucose meter testing. From a research perspective, advancing progress in these areas is as simple as discovering the complete protective potential of the basic technology.
Schedule a chat to discuss >>Together we can build the future of low-cost, edge/consumer diagnostics.
An advanced toolkit of mathematics and statistics for the future of diagnostic medicine
Diagnosing cancer can be easy using advances in statistics and mathematics (including machine learning); modern advances present novel opportunities such as those pertaining to the intelligent use of large amounts of data or algorithms that carry over from ongoing revolutions in technology (such as in imaging).
Anomaly Detection and Time-Series
Anomaly detection can be useful in this domain in various ways. One may be the identification of new markers on the basis of their being anomalous (e.g. in a external population or self/internal temporal/variational sense). Another may be diagnostics on a similar basis whereupon an anomalous event occurs over the course of a time-series (using change through time as an indicator).
Time-series with self standards
Related to anomaly detection is the use of the individual person as a standard. This standard can be considered in the time-variational sense (does observed variation diverge from normal variation). 'Potential issue' may be defined as simply something that diverges from normal temporal variation (previously having modeled this) such as self-relative-unusual (not necessarily pattern matching) divergence in RNA expression.
NGS may be the future of cfDNA
Transitioning from a panel of known markers to the processing of whole genome data and intelligent or learned inferences regarding these may be an informational transition that facilitates a future-generation advance. Related to the previous point on anomaly detection, rare or original mutations that aren't detectable using established markers may still present as significantly anomalous.
Advanced Clustering
High-dimensional relationships between person characteristics, markers, and outcomes may be more complicated than "marker predicts outcome" but rather more like there exist high-dimensional regions in characteristic+marker space that correspond to certain outcomes. We may be able to learn and validate these in the form of interpretable predictive models that produce explorable feature spaces as intermediate result.
Rapid Prototyping
We have the tools and experience to prototype innovative methods quickly, giving you answers about usefulness before committing.
Schedule a chat to discuss >>Accelerated Implementations
Our development process supports rapid translation of prototype methods into (sometimes vastly) accelerated forms using GPUs and FPGAs.
Schedule a chat to discuss >>Read the idea whitepaper
Get inspired by reading about some of our ideas as well as ways to pilot these; learn further how we can work together to advance the state of the art through scholarly collaboration.
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Medicine of the future will be informed by advanced data and inference.
Together we can build and deliver that future.