| Summary: | Precision medicine (PM) is a branch of medicine that defines a disease at a higher
resolution using genetic and other technologies to enable more specific targeting of its
subgroups. Because of its uses in clinical treatment and diagnostics, this field exemplifies
the modern era of medicine. PM looks for not just the right drug, but also the right dosage
and treatment regimen. PM encounters a variety of challenges, which will be explored in
this dissertation.
Large-scale sensitivity screens and whole-exome sequencing experiments (WES) have
fostered a new wave of targeted treatments based on finding associations between drug
sensitivity and response biomarkers. These experiments with the aid of state-of-the-art
artificial intelligence (AI) algorithms are opening new therapeutic opportunities for diseases
with unmet clinical needs. It has been proved that AI is capable of predicting novel
personalized treatments based on complex genotypic and phenotypic patterns in tumors.
The scientific community should make an effort to make these algorithms to be interpretable
to humans so that the results could be easily approved by the medical regulators. The
purpose of this thesis is to apply AI algorithms for precision oncology that are highly
accurate, while guaranteeing that the predictions are interpretable by humans.
This work is divided in three main sections. The first section comprises a new methodology
to increase the predictive power of the discovery of novel treatments in large-scale
screenings by exploiting that some biomarkers tend to appear in many treatments. This fact
is called hub effect in gene essentiality (HUGE). Content of this section was published in
[1]. The second section contains a novel interpretable AI method -called multi-dimensional
module optimization (MOM)- that associates drug screening with genetic events and
proposes a treatment guideline. Content of this section was published in [2]. Finally, the
third section includes a detailed comparison of different recently published algorithms that
attempt to overcome the barriers proposed by today's precision medicine. This study also
includes two novel algorithms specifically designed to solve the challenges of applicability
to clinical practice: Optimal Decision Tree (ODT) and Multinomial Lasso.
The characterization of Interpretable Artificial Intelligence as approach with strong potential
for use in clinical practice is one of the study's most significant achievements. We presen tunique methods for PM that are highly interpretable, and we summarize the needs that
could be considered for constructing interpretable AI. We are confident that this method will
transform the way PM is addressed, bridging the gap between AI and clinical practice.
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