2 research outputs found
Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through <i>In Silico</i> Modeling
Molecular fingerprinting via vibrational spectroscopy
characterizes
the chemical composition of molecularly complex media which enables
the classification of phenotypes associated with biological systems.
However, the interplay between factors such as biological variability,
measurement noise, chemical complexity, and cohort size makes it challenging
to investigate their impact on how the classification performs. Considering
these factors, we developed an in silico model which
generates realistic, but configurable, molecular fingerprints. Using
experimental blood-based infrared spectra from two cancer-detection
applications, we validated the model and subsequently adjusted model
parameters to simulate diverse experimental settings, thereby yielding
insights into the framework of molecular fingerprinting. Intriguingly,
the model revealed substantial improvements in classifying clinically
relevant phenotypes when the biological variability was reduced from
a between-person to a within-person level and when the chemical complexity
of the spectra was reduced. These findings quantitively demonstrate
the potential benefits of personalized molecular fingerprinting and
biochemical fractionation for applications in health diagnostics
Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through <i>In Silico</i> Modeling
Molecular fingerprinting via vibrational spectroscopy
characterizes
the chemical composition of molecularly complex media which enables
the classification of phenotypes associated with biological systems.
However, the interplay between factors such as biological variability,
measurement noise, chemical complexity, and cohort size makes it challenging
to investigate their impact on how the classification performs. Considering
these factors, we developed an in silico model which
generates realistic, but configurable, molecular fingerprints. Using
experimental blood-based infrared spectra from two cancer-detection
applications, we validated the model and subsequently adjusted model
parameters to simulate diverse experimental settings, thereby yielding
insights into the framework of molecular fingerprinting. Intriguingly,
the model revealed substantial improvements in classifying clinically
relevant phenotypes when the biological variability was reduced from
a between-person to a within-person level and when the chemical complexity
of the spectra was reduced. These findings quantitively demonstrate
the potential benefits of personalized molecular fingerprinting and
biochemical fractionation for applications in health diagnostics