BioEngine4D™

Diseases "signal" themselves through multiple modalities — molecular and non-molecular.

Many of these diseases develop while "signaling" themselves through non-molecular pathways, depending on the disease, typically as subtle, occasional, or intermittent physiological or behavioral "signs." As humans, we subsequently observe these signs as symptoms, often later in a disease's stage, and sometimes too late.

Our health is dynamic. Our measures of health should also be dynamic.

LIFEdata™ will provide more objective, convenient, affordable, and frequent insights into our dynamic health.

We define LIFEdata as sensor-measured information digitally generated from life and around life. We are connecting LIFEdata as learnable insights from expressions and environment to disease detection and drug response through machine learning and artificial intelligence.

Do insights into our health still need to be so limited to the Medical Setting?

No. The signs of many diseases do not need to be limited to molecular measures, but can and should be measured non-molecularly (phenotypically) as well. LIFEdata can be generated without needing to physically or invasively "access" an intermediary such as blood or tissue – yielding unprecedented scalability – a key hallmark of the digital revolution.

Dimentionality of Time and Measures need to be reimagined.

The time dimension has been largely absent in our approaches to healthcare today.

Once-a-year or intermittent medical visits (i.e. snapshot "measures") are ineffective. Many developing diseases have a dynamic pattern of progression that can be more timely detected through more frequent physiological measures. Earlier detection catalyzes timelier diagnoses and interventions, can yield improved clinical outcomes and treatment cost savings – or ROeI (Return On earlier Intervention) – for the net system as a whole.

Healthcare has also been hindered by a key limitation of the human brain: the urge to measure and process in low dimensions.

Although advances in technology over the years have been able to radically disrupt most industries, there remains a gap within healthcare, and we believe many solutions and synergies exist locked within the Life setting.

Consumer smart devices with sensors that can capture LIFEdata are starting to rapidly populate our Life settings. These digital devices provide individuals with the opportunity to contribute to and access novel healthcare innovations outside the Medical setting. Even IoT home devices not originally purposed for healthcare applications can be a very valuable source of LIFEdata which will be processed by our technology platform. We believe subtle and quantifiable, non-molecular and "digitizable" signals are under-discovered, but very insightful objective "markers" for many developing diseases and disorders.

The health potential of the smartphone is still in its infancy. Over 3 billion human beings use a smartphone as a computationally augmented extention of their bodies.

Smartphones, smartwatches and smart devices provide the Consumer a number of sensory modalities to more conveniently, frequently, and affordably generate LIFEdata. Existing chips, hardware, and sensors already in your smart devices have already put powerful "tools" in your pocket - novel and digitally downloadable software can enable their full potential as "health tools." And these smart devices become more powerful each year, effectively broadening their health application potential.

We have only just begun to scratch the surface with the number of possibilities: n! / r! * (n − r)! ...

We are developing a health technology platform called BioEngine4D™ which leverages machine learning and AI to analytically convert LIFEdata into digital biomarkers of disease with applications for data driven drug development and digitally detecting developing diseases.

Medical data has traditionally been generated in snapshots. LIFEdata can be generated around us passively and frequently. Each LIFEdata category has its own advantages and disadvantages; however, they are limited in perspective if analyzed alone and synergistic in value when analyzed interconnectedly.
LIFEdata is inherently digital, serving as the foundation of novel digital biomarkers for many key diseases.
Diseases and their signs and symptoms are also interconnected among multiple overlapping physiologic systems. BioTrillion believes that the application of multivariate analyses and AI will enable the discovery of causal connections between physiologic systems not previously known.

The Social Graph of Disease Interconnectivity

BioTrillion is developing radically novel digital biomarkers by algorithmically training BioEngine4D via machine learning and artificial intelligence — premised around clinically-known and causal pathophysiologies.

BioEngine4D, fueled by Medical data and LIFEdata, is being developed to enable, even enhance, the Consumer's current self-healthcare abilities.

As our platform's Users generate more types of LIFEdata, they can begin to serve as a central data portal and reposition themselves closer to the center of the healthcare paradigm.

We are developing BioEngine4D to enable us to digitally detect disease signs that are currently too subtle for an individual's or doctor's observational abilities, leading to more timely intervention. And digitally measure a disease's response to new drugs in clinical trials, leading to accelerated drug development.

Our solution to enhanced disease detection and drug response measures is largely predicated on the novelty, complexity, and accuracy of BioEngine4D. Moreover, BioEngine4D becomes maximally commercializable with a front-end App layer that is versatile for both Consumers and Medical professionals, intuitive to use, and value-additive to adopt.

BioEngine4D Vertical and Horizontal Platform

It's time to reimagine Healthcare solutions around accessibility, timeliness, and quantitatively enhanced outcomes.

Solutions that will make us realize our health had been in the dark 99% of the time in retrospect.

BioTrillion's mission is to transform Data from Life into Data for Life.

Measures of diseases must shift from qualitative to quantitative to computable to scalable. The solution is looking at you every day.