Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

Esteban-Medina, Marina; Peña‐Chilet, María; Loucera, Carlos; Dopazo, Joaquı́n

doi:10.21203/rs.2.9615/v2

Cited by 7 publications

(9 citation statements)

References 39 publications

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“…The paper uses the METABRIC breast cancer as a proof of concept and allows to see promising directions to build patient models with mechanistic insights. Esteban-Medina et al, [8] used public gene expression data and a list of genes that are the target of approved drugs to identify potential causal relationships between proteins and cell activities. They rely on a Multi-Output Random Forest regressor available in scikit-learn and an optimization strategy built on top to predict a circuit activity across the disease pathway.…”

Section: Trend 1: Approaches Based On Machine-learning Methodsmentioning

confidence: 99%

“…Esteban-Medina et al,. [8] used a machine learning approach, combined with data from KEGG, Orphanet, GEO, GTEx and DrugBank to identify drugs that could have an effect on signaling the circuits that cause the treatment of the Fanconi anemia. Selected in the top-4 papers, Chen et al, [6] used a neural network to predict drug resistance to antibiotics in Mycobacterium tuberculosis.…”

Section: Trend 3: Drug Repositioning and Large-scale Prediction Of Drmentioning

confidence: 99%

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Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

Smaïl-Tabbone

Rance

2020

Yearb Med Inform

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Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

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Section: Trend 1: Approaches Based On Machine-learning Methodsmentioning

confidence: 99%

Section: Trend 3: Drug Repositioning and Large-scale Prediction Of Drmentioning

confidence: 99%

Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

Smaïl-Tabbone

Rance

2020

Yearb Med Inform

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“…Therefore, this opens the possibility of using these models for exploring new therapeutic options as well (22).…”

Section: Introductionmentioning

confidence: 99%

Mechanistic modeling of the SARS-CoV-2 disease map

Rian

Esteban-Medina

Hidalgo

et al. 2020

Preprint

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Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.

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“…Therefore, a proper interpretation of the effect that differences in gene expression have over phenotypes, such as drug response or disease progression, involves understanding the mechanisms of the disease or the mode of action of drugs, which can be interpreted through mechanistic models of cell signaling [ 12 ] or cell metabolism [ 13 ]. Mechanistic models have helped to understand the disease mechanisms behind different cancers [ 14 , 15 ], including neuroblastoma [ 16 , 17 ], breast cancer [ 18 ], rare diseases [ 19 ], complex diseases [ 20 ], the mechanisms of action of drugs [ 21 , 22 ], and other biologically interesting scenarios such as the molecular mechanisms that explain how stress-induced activation of brown adipose tissue prevents obesity [ 23 ] or the molecular mechanisms of death and the post-mortem ischemia of a tissue [ 24 ]. Among the few available proposals of mechanistic modeling algorithms that model different aspects of signaling pathway activity, Hipathia has demonstrated having superior sensitivity and specificity [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

Çubuk

Can

Peña‐Chilet

et al. 2020

Cells

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Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.

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Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

Cited by 7 publications

References 39 publications

Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

Mechanistic modeling of the SARS-CoV-2 disease map

Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

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