We analyze data with a translational focus to support your research pipeline

Our team tackles complex data science problems in a wide variety of areas

We work directly with the principal investigator to find the best statistical approaches for the data they're generating, especially when their research produces very complex or high dimensional data. We focus on supporting research from the lab to the clinic or farm.

Expert Statistical Consulting

Our statisticians and programmers are focused on finding the most effective way to get answers from your data. Our clients, ranging from big pharma to small biotech, have access to seasoned statisticians that work collaboratively with experts so statistical conclusions line up with biological signals.

Statistical protocol development

Statistical analysis plans

Study design

Applied data analysis

Pipeline strategy

Clinical trial support

FDA application statistical support

-omics Analytics

We have extensive expertise statistically analyzing -omic and multi-omic datasets for human studies. Since the beginning of the HMP, we have been on the forefront of developing statistical methods to give you the most accurate results.

microbiology-technician-working-with-bac

Microbiome

laboratory-test-tubes-filled-with-liquid

Untargeted Metabolomics

Multi-omics

When your studies use more than one omic technology, we can statistically analyze it using integrative methods that bring all your data together into one analysis.

Our statisticians have developed a way to detect peaks in untargeted metabolomics data automatically, within a few hours, and without bias.

Traditional statistics are usually inaccurate or incomplete if used with microbiome data. Our approach normalizes this data so classical statistical methods apply.

Hypothesis testing

Regression

Power and sample size calculations

Full LC/MS core support

Automatic peak detection

Significant peak difference

Unbiased hypothesis testing

Dose response

Pathway analysis

Regression

Translational outcomes

Internet-of-things (IoT) sensor data

We have developed statistics-based approaches for sensor data that limit the need for closed-box software

Instead of using boxed AI software that trains on massive, labeled historical datasets, our approach analyzes the data from your sensors prospectively, ensuring every analysis fits your specific situation and changes as conditions change.

This technology, developed in BioRankings, is currently being commercialized into a point-of-care medical device by Spoke Analytics. It is available to BioRankings consulting clients now.

Picks up on small signals to find changes earlier

Uses an patient's own data as its collected

Uses very little compute power or storage

Sensors can be added or dropped without heavy retraining

We analyze data with a translational focus to support your research pipeline

Our team tackles complex data science problems in a wide variety of areas

We work directly with the principal investigator to find the best statistical approaches for the data they're generating, especially when their research produces very complex or high dimensional data. We focus on supporting research from the lab to the clinic or farm.

Expert Statistical Consulting

Statistical protocol development

Statistical analysis plans

Study design

Applied data analysis

Pipeline strategy

Clinical trial support

FDA application statistical support

​

-omics Analytics

We have extensive expertise statistically analyzing -omic and multi-omic datasets for human studies. Since the beginning of the HMP, we have been on the forefront of developing statistical methods to give you the most accurate results.

Microbiome

Untargeted Metabolomics

Multi-omics

When your studies use more than one omic technology, we can statistically analyze it using integrative methods that bring all your data together into one analysis.

Our statisticians have developed a way to detect peaks in untargeted metabolomics data automatically, within a few hours, and without bias.

Traditional statistics are usually inaccurate or incomplete if used with microbiome data. Our approach normalizes this data so classical statistical methods apply.

Hypothesis testing

Regression

Power and sample size calculations

Full LC/MS core support

Automatic peak detection

Significant peak difference

Unbiased hypothesis testing

Dose response

Pathway analysis

Regression

Translational outcomes

Internet-of-things (IoT) sensor data

We have developed statistics-based approaches for sensor data that limit the need for closed-box software

Instead of using boxed AI software that trains on massive, labeled historical datasets, our approach analyzes the data from your sensors prospectively, ensuring every analysis fits your specific situation and changes as conditions change.

​

This technology, developed in BioRankings, is currently being commercialized into a point-of-care medical device by Spoke Analytics. It is available to BioRankings consulting clients now.