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Article ; Online: Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models within Cardiology.

Nolin Lapalme, Alexis / Corbin, Denis / Tastet, Olivier / Avram, Robert / Hussin, Julie G

2024

Abstract: In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. This review explores the complex issue of data bias, specifically addressing those encountered ... ...

Abstract	In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. This review explores the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and impacts of these biases, which challenge their reliability and widespread applicability in healthcare. Using a case study, we highlight the complexities involved in addressing these biases from a clinical viewpoint. The goal of this review is to equip researchers and clinicians with the practical knowledge needed to identify, understand, and mitigate these biases, advocating for the creation of AI solutions that are not just technologically sound, but also fair and effective for all patient demographics.
Language	English
Publishing date	2024-05-10
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	632813-1
ISSN	1916-7075 ; 0828-282X
ISSN (online)	1916-7075
ISSN	0828-282X
DOI	10.1016/j.cjca.2024.04.026
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Article ; Online: Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data

Mostefai, Fatima / Grenier, Jean-Christophe / Poujol, Raphäel / Hussin, Julie G.

bioRxiv

Abstract: Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral ... ...

Abstract	Understanding the evolution of viral genomes is essential for elucidating how viruses adapt and change over time. Analyzing intra-host single nucleotide variants (iSNVs) provides key insights into the mechanisms driving the emergence of new viral lineages, which is crucial for predicting and mitigating future viral threats. Despite the potential of next-generation sequencing (NGS) to capture these iSNVs, the process is fraught with challenges, particularly the risk of capturing sequencing artifacts that may result in false iSNVs. To tackle this issue, we developed a two-step workflow designed to enhance the reliability of iSNV detection in large heterogeneous collections of NGS libraries. We use over 130,000 publicly available SARS-CoV-2 NGS libraries to show how our comprehensive workflow effectively distinguishes emerging viral mutations from sequencing errors. This approach incorporates rigorous bioinformatics protocols, stringent quality control metrics, and innovative usage of dimensionality reduction methods to generate insightful representations of this high-dimensional dataset. We identified and mitigated notable batch effects linked to specific sequencing centers around the world and introduced quality control metrics such as the Strand Bias Likelihood that considers strand coverage imbalance, enhancing iSNV reliability. Additionally, we pioneer the application of the PHATE visualization approach to genomic data and introduce a methodology that quantifies how closely related groups of data points are within a two-dimensional space, enhancing our ability to explain clustering patterns based on their shared genetic characteristics. Our workflow not only sheds light on the complexities of viral genomic analysis with state-of-the-art sequencing technologies but also advances the detection of accurate intra-host mutations, opening the door for an enhanced understanding of viral adaptation mechanisms.
Keywords	covid19
Language	English
Publishing date	2024-05-01
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2024.04.26.591384
Database	COVID19

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Article ; Online: Comparative Study of Protein Aggregation Propensity and Mutation Tolerance Between Naked Mole-Rat and Mouse.

Besse, Savandara / Poujol, Raphaël / Hussin, Julie G

Genome biology and evolution

2022 Volume 14, Issue 5

Abstract: The molecular mechanisms of aging and life expectancy have been studied in model organisms with short lifespans. However, long-lived species may provide insights into successful strategies for healthy aging, potentially opening the door for novel ... ...

Abstract	The molecular mechanisms of aging and life expectancy have been studied in model organisms with short lifespans. However, long-lived species may provide insights into successful strategies for healthy aging, potentially opening the door for novel therapeutic interventions in age-related diseases. Notably, naked mole-rats, the longest-lived rodent, present attenuated aging phenotypes compared with mice. Their resistance toward oxidative stress has been proposed as one hallmark of their healthy aging, suggesting their ability to maintain cell homeostasis, specifically their protein homeostasis. To identify the general principles behind their protein homeostasis robustness, we compared the aggregation propensity and mutation tolerance of naked mole-rat and mouse orthologous proteins. Our analysis showed no proteome-wide differential effects in aggregation propensity and mutation tolerance between these species, but several subsets of proteins with a significant difference in aggregation propensity. We found an enrichment of proteins with higher aggregation propensity in naked mole-rat, and these are functionally involved in the inflammasome complex and nucleic acid binding. On the other hand, proteins with lower aggregation propensity in naked mole-rat have a significantly higher mutation tolerance compared with the rest of the proteins. Among them, we identified proteins known to be associated with neurodegenerative and age-related diseases. These findings highlight the intriguing hypothesis about the capacity of the naked mole-rat proteome to delay aging through its proteomic intrinsic architecture.
MeSH term(s)	Animals ; Longevity/genetics ; Mice ; Mole Rats/genetics ; Mutation ; Protein Aggregates ; Proteome/genetics ; Proteomics
Chemical Substances	Protein Aggregates ; Proteome
Language	English
Publishing date	2022-04-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2495328-3
ISSN	1759-6653 ; 1759-6653
ISSN (online)	1759-6653
ISSN	1759-6653
DOI	10.1093/gbe/evac057
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Article ; Online: Toward computing attributions for dimensionality reduction techniques.

Scicluna, Matthew / Grenier, Jean-Christophe / Poujol, Raphaël / Lemieux, Sébastien / Hussin, Julie G

Bioinformatics advances

2023 Volume 3, Issue 1, Page(s) vbad097

Abstract: Summary: We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification-using the gradients of target outputs with ... ...

Abstract	Summary: We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification-using the gradients of target outputs with respect to the inputs-on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets. Availability and implementation: We have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne.
Language	English
Publishing date	2023-08-03
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbad097
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Article ; Online: deepSimDEF: deep neural embeddings of gene products and gene ontology terms for functional analysis of genes.

Pesaranghader, Ahmad / Matwin, Stan / Sokolova, Marina / Grenier, Jean-Christophe / Beiko, Robert G / Hussin, Julie

Bioinformatics (Oxford, England)

2022 Volume 38, Issue 11, Page(s) 3051–3061

Abstract: Motivation: There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein-protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application- ... ...

Abstract	Motivation: There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein-protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations. Results: We introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF's key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations: it outperformed all other FS measures by >5-10% on yeast and human reference datasets on protein-protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism. Availability and implementation: Source code and data are available at https://github.com/ahmadpgh/deepSimDEF. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Humans ; Gene Ontology ; Computational Biology/methods ; Proteins ; Molecular Sequence Annotation ; Software ; Saccharomyces cerevisiae ; RNA
Chemical Substances	Proteins ; RNA (63231-63-0)
Language	English
Publishing date	2022-05-22
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac304
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Article ; Online: The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.

Gazeau, Sonia / Deng, Xiaoyan / Ooi, Hsu Kiang / Mostefai, Fatima / Hussin, Julie / Heffernan, Jane / Jenner, Adrianne L / Craig, Morgan

Immunoinformatics (Amsterdam, Netherlands)

2023 Volume 9, Page(s) 100021

Abstract: The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve ... ...

Abstract	The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
Language	English
Publishing date	2023-01-08
Publishing country	Netherlands
Document type	Journal Article ; Review
ISSN	2667-1190
ISSN (online)	2667-1190
DOI	10.1016/j.immuno.2023.100021
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Article ; Online: Signatures of Co-evolution and Co-regulation in the CYP3A and CYP4F Genes in Humans.

Richard-St-Hilaire, Alex / Gamache, Isabel / Pelletier, Justin / Grenier, Jean-Christophe / Poujol, Raphaël / Hussin, Julie G

Genome biology and evolution

2023 Volume 16, Issue 1

Abstract: Cytochromes P450 (CYP450) are hemoproteins generally involved in the detoxification of the body of xenobiotic molecules. They participate in the metabolism of many drugs and genetic polymorphisms in humans have been found to impact drug responses and ... ...

Abstract	Cytochromes P450 (CYP450) are hemoproteins generally involved in the detoxification of the body of xenobiotic molecules. They participate in the metabolism of many drugs and genetic polymorphisms in humans have been found to impact drug responses and metabolic functions. In this study, we investigate the genetic diversity of CYP450 genes. We found that two clusters, CYP3A and CYP4F, are notably differentiated across human populations with evidence for selective pressures acting on both clusters: we found signals of recent positive selection in CYP3A and CYP4F genes and signals of balancing selection in CYP4F genes. Furthermore, an extensive amount of unusual linkage disequilibrium is detected in this latter cluster, indicating co-evolution signatures among CYP4F genes. Several of the selective signals uncovered co-localize with expression quantitative trait loci (eQTL), which could suggest epistasis acting on co-regulation in these gene families. In particular, we detected a potential co-regulation event between CYP3A5 and CYP3A43, a gene whose function remains poorly characterized. We further identified a causal relationship between CYP3A5 expression and reticulocyte count through Mendelian randomization analyses, potentially involving a regulatory region displaying a selective signal specific to African populations. Our findings linking natural selection and gene expression in CYP3A and CYP4F subfamilies are of importance in understanding population differences in metabolism of nutrients and drugs.
MeSH term(s)	Animals ; Humans ; Cytochrome P-450 CYP3A/genetics ; Cytochrome P-450 CYP3A/metabolism ; Hominidae/metabolism ; Cytochrome P-450 Enzyme System/genetics ; Polymorphism, Genetic ; Selection, Genetic
Chemical Substances	Cytochrome P-450 CYP3A (EC 1.14.14.1) ; Cytochrome P-450 Enzyme System (9035-51-2)
Language	English
Publishing date	2023-11-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2495328-3
ISSN	1759-6653 ; 1759-6653
ISSN (online)	1759-6653
ISSN	1759-6653
DOI	10.1093/gbe/evad236
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Article ; Online: Construction of a femininity score in the UK Biobank and its association with angina diagnosis prior to myocardial infarction.

Levinsson, Anna / de Denus, Simon / Sandoval, Johanna / Lemieux Perreault, Louis-Philippe / Rouleau, Joëlle / Tardif, Jean-Claude / Hussin, Julie / Dubé, Marie-Pierre

Scientific reports

2022 Volume 12, Issue 1, Page(s) 1780

Abstract: Gender captures social components beyond biological sex and can add valuable insight to health studies in populations. However, assessment of gender typically relies on questionnaires which may not be available. The aim of this study is to construct a ... ...

Abstract	Gender captures social components beyond biological sex and can add valuable insight to health studies in populations. However, assessment of gender typically relies on questionnaires which may not be available. The aim of this study is to construct a gender metric using available variables in the UK Biobank and to apply it to the study of angina diagnosis. Proxy variables for femininity characteristics were identified in the UK Biobank and regressed on sex to construct a composite femininity score (FS) validated using tenfold cross-validation. The FS was assessed as a predictor of angina diagnosis before incident myocardial infarction (MI) events. The FS was derived for 315,937 UK Biobank participants. In 3059 individuals with no history of MI at study entry who had an incident MI event, the FS was a significant predictor of angina diagnosis prior to MI (OR 1.24, 95% CI 1.10-1.39, P < 0.001) with a significant sex-by-FS interaction effect (P = 0.003). The FS was positively associated with angina diagnosis prior to MI in men (OR 1.37, 95% CI 1.19-1.57, P < 0.001), but not in women. We have provided a new tool to conduct gender-sensitive analyses in observational studies, and applied it to study of angina diagnosis prior to MI.
MeSH term(s)	Angina Pectoris/diagnosis ; Angina Pectoris/epidemiology ; Biological Specimen Banks/statistics & numerical data ; Female ; Femininity ; Humans ; Male ; Middle Aged ; Myocardial Infarction/physiopathology ; Risk Assessment/methods ; Risk Factors ; Sex Factors ; United Kingdom/epidemiology
Language	English
Publishing date	2022-02-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-05713-x
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Article ; Online: Study of effect modifiers of genetically predicted CETP reduction.

Legault, Marc-André / Barhdadi, Amina / Gamache, Isabel / Lemaçon, Audrey / Lemieux Perreault, Louis-Philippe / Grenier, Jean-Christophe / Sylvestre, Marie-Pierre / Hussin, Julie G / Rhainds, David / Tardif, Jean-Claude / Dubé, Marie-Pierre

Genetic epidemiology

2023 Volume 47, Issue 2, Page(s) 198–212

Abstract: Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and ... ...

Abstract	Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers in UK Biobank participants. Female sex and lower BMI were associated with higher high-density lipoprotein cholesterol and lower low-density lipoprotein cholesterol for the same genetically predicted reduction in CETP concentration. We found that sex also modulated the effect of genetically lower CETP on cholesterol efflux capacity in samples from the Montreal Heart Institute Biobank. However, these modifying effects did not extend to sex differences in cardiovascular outcomes in our data. Our results provide insight into the clinical effects of CETP inhibitors in the presence of effect modification based on genetic data. The approach can support precision medicine applications and help assess the external validity of clinical trials.
MeSH term(s)	Humans ; Male ; Female ; Cholesterol Ester Transfer Proteins/genetics ; Cholesterol, HDL ; Cholesterol, LDL ; Biomarkers
Chemical Substances	Cholesterol Ester Transfer Proteins ; Cholesterol, HDL ; Cholesterol, LDL ; Biomarkers ; CETP protein, human
Language	English
Publishing date	2023-01-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22514
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Article ; Online: ImputeCoVNet: 2D ResNet Autoencoder for Imputation of SARS-CoV-2 Sequences

Pesaranghader, Ahmad / Pelletier, Justin / Grenier, Jean-Christophe / Poujol, Raphaël / Hussin, Julie

bioRxiv

Abstract: We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ... ...

Abstract	We describe a new deep learning approach for the imputation of SARS-CoV-2 variants. Our model, ImputeCoVNet, consists of a 2D ResNet Autoencoder that aims at imputing missing genetic variants in SARS-CoV-2 sequences in an efficient manner. We show that ImputeCoVNet leads to accurate results at minor allele frequencies as low as 0.0001. When compared with an approach based on Hamming distance, ImputeCoVNet achieved comparable results with significantly less computation time. We also present the provision of geographical metadata (e.g., exposed country) to decoder increases the imputation accuracy. Additionally, by visualizing the embedding results of SARS-CoV-2 variants, we show that the trained encoder of ImputeCoVNet, or the embedded results from it, recapitulates viral clade9s information, which means it could be used for predictive tasks using virus sequence analysis.
Keywords	covid19
Language	English
Publishing date	2021-08-16
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2021.08.13.456305
Database	COVID19

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