Summary
Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics
are gradually converging into a larger field that we call computational biomedicine.
Objectives: Developing a computational framework that is common to the different disciplines
that compose computational biomedicine will be a major enabler of the further development
and integration of this research domain.
Methods: Probabilistic graphical models such as Hidden Markov Models, belief networks, and
missing-data models together with computational methods such as dynamic programming,
Expectation-Maximization, data-augmentation Gibbs sampling, and the Metropolis-Hastings
algorithm provide the tools for an integrated probabilistic approach to computational
biomedicine.
Results and Conclusions: We show how graphical models have already found a broad application in different
fields composing computational biomedicine. We also indicate several challenges that
lie at the interface between medical informatics, statistical genomics, and bioinformatics.
We also argue that graphical models offer a unified framework making it possible to
integrate in a statistically meaningful way multiple models ranging from the molecular level to cellular and to clinical levels.
Because of their versatility and firm statistical underpinning, we assert that probabilistic
graphical models can serve as the lingua franca for many computationally intensive approaches to biology and medicine. As such, graphical
models should be a foundation of the curriculum of students in these fields. From
such a foundation, students could then build towards specific computational methods
in medical informatics, medical image analysis, statistical genetics, or bioinformatics
while keeping the communication open between these areas.
Keywords
Probabilistic graphical models - belief networks - Expectation-Maximization - Gibbs
sampling - medical informatics - statistical genetics - bioinformatics - computational
biomedicine
