Interpretable precision medicine for acute myeloid leukemia
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 l...
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| Format: | info:eu-repo/semantics/doctoralThesis |
| Language: | eng |
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Servicio de Publicaciones. Universidad de Navarra
2023
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| Online Access: | https://hdl.handle.net/10171/65641 |
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| author | Gimeno-Combarro, M. (Marian) Rubio-Díaz-Cordovés, Á. (Ángel) Carazo-Melo, F.(Fernando) |
| author_facet | Gimeno-Combarro, M. (Marian) Rubio-Díaz-Cordovés, Á. (Ángel) Carazo-Melo, F.(Fernando) |
| author_sort | Gimeno-Combarro, M. (Marian) |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/doctoralThesis |
| id | oai:dadun.unav.edu:10171-65641 |
| institution | Universidad de Navarra |
| language | eng |
| publishDate | 2023 |
| publisher | Servicio de Publicaciones. Universidad de Navarra |
| record_format | dspace |
| spelling | oai:dadun.unav.edu:10171-656412023-03-13T06:12:04Z Interpretable precision medicine for acute myeloid leukemia Gimeno-Combarro, M. (Marian) Rubio-Díaz-Cordovés, Á. (Ángel) Carazo-Melo, F.(Fernando) Precision medicine Biomarker Drug repositioning Myeloid leukemia 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. 2023-03-08T15:05:25Z 2023-03-08T15:05:25Z 2023-03 info:eu-repo/semantics/doctoralThesis https://hdl.handle.net/10171/65641 eng info:eu-repo/semantics/openAccess application/pdf Servicio de Publicaciones. Universidad de Navarra |
| spellingShingle | Precision medicine Biomarker Drug repositioning Myeloid leukemia Gimeno-Combarro, M. (Marian) Rubio-Díaz-Cordovés, Á. (Ángel) Carazo-Melo, F.(Fernando) Interpretable precision medicine for acute myeloid leukemia |
| title | Interpretable precision medicine for acute myeloid leukemia |
| title_full | Interpretable precision medicine for acute myeloid leukemia |
| title_fullStr | Interpretable precision medicine for acute myeloid leukemia |
| title_full_unstemmed | Interpretable precision medicine for acute myeloid leukemia |
| title_short | Interpretable precision medicine for acute myeloid leukemia |
| title_sort | interpretable precision medicine for acute myeloid leukemia |
| topic | Precision medicine Biomarker Drug repositioning Myeloid leukemia |
| url | https://hdl.handle.net/10171/65641 |
| work_keys_str_mv | AT gimenocombarrommarian interpretableprecisionmedicineforacutemyeloidleukemia AT rubiodiazcordovesaangel interpretableprecisionmedicineforacutemyeloidleukemia AT carazomeloffernando interpretableprecisionmedicineforacutemyeloidleukemia |