@article {Asaad2022,
	title = {AsthmaKGxE: An asthma{\textendash}environment interaction knowledge graph leveraging public databases and scientific literature},
	journal = {Computers in Biology and Medicine},
	volume = {148},
	year = {2022},
	note = {cited By 1},
	abstract = {Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma{\textendash}Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog{\textquoteright}s most substantiated results are then integrated into the KG. Results: The resulting environmentally rich knowledge graph {\textquotedblright}AsthmaKGxE{\textquotedblright} aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma{\textendash}environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability: A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE). {\textcopyright} 2022 Elsevier Ltd},
	keywords = {allergen, Article, Artificial intelligence, Association reactions, asthma, Automated, automated pattern recognition, data base, data extraction, Databases, Diseases, environmental factor, Factual, factual database, Gene-Environment Interaction, Genetic factors, genotype environment interaction, Graphic methods, Heterogeneous disease, human, Humans, Knowledge graph, Knowledge graphs, Knowledge management, Language processing, Learning algorithms, Machine learning, Machine-learning, NAtural language processing, Natural language processing systems, Natural languages, nutritional assessment, obesity, pathophysiology, Pattern recognition, pharmacogenetics, physiological stress, psychological aspect, Public database, Scientific literature, socioeconomics, User interfaces},
	doi = {10.1016/j.compbiomed.2022.105933},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135702024\&doi=10.1016\%2fj.compbiomed.2022.105933\&partnerID=40\&md5=2022f55e1de0bbaa947ba6699af6a143},
	author = {Asaad, C. and Ghogho, M.}
}
@article {Gryech2022,
	title = {An Exploration of Features Impacting Respiratory Diseases in Urban Areas},
	journal = {International Journal of Environmental Research and Public Health},
	volume = {19},
	number = {5},
	year = {2022},
	note = {cited By 3},
	abstract = {Air pollution exposure has become ubiquitous and is increasingly detrimental to human health. Small Particulate matter (PM) is one of the most harmful forms of air pollution. It can easily infiltrate the lungs and trigger several respiratory diseases, especially in vulnerable populations such as children and elderly people. In this work, we start by leveraging a retrospective study of 416 children suffering from respiratory diseases. The study revealed that asthma prevalence was the most common among several respiratory diseases, and that most patients suffering from those diseases live in areas of high traffic, noise, and greenness. This paved the way to the construction of the MOREAIR dataset by combining feature abstraction and micro-level scale data collection. Unlike existing data sets, MOREAIR is rich in context-specific components, as it includes 52 temporal or geographical features, in addition to air-quality measurements. The use of Random Forest uncovered the most important features for the understanding of air-quality distribution in Moroccan urban areas. By linking the medical data and the MOREAIR dataset, we observed that the patients included in the medical study come mostly from neighborhoods that are characterized by either high average or high variations of pollution levels. {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {aged, air pollutant, Air Pollutants, Air pollution, Air quality, air quality control, Article, asthma, breathing disorder, child, controlled study, disease risk assessment, environmental exposure, environmental risk, exploratory research, geographic information system, GIS, health risk, human, Humans, major clinical study, Measurement, medical information, Moroccan, neighborhood, noise injury, Particulate Matter, prevalence, public health, random forest, Respiration Disorders, respiratory disease, respiratory tract disease, Retrospective Studies, retrospective study, Risk assessment, risk factor, traffic pollution, urban area},
	doi = {10.3390/ijerph19053095},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125596721\&doi=10.3390\%2fijerph19053095\&partnerID=40\&md5=011cdce4b84286477d86b54655147f71},
	author = {Gryech, I. and Ghogho, M. and Mahraoui, C. and Kobbane, A.}
}
@article {Pefura-Yone2020297,
	title = {Atopic dermatitis in children and adults in Sub-saharan Africa and Maghreb [{\'E}tat des lieux de la dermatite atopique de l{\textquoteright}enfant et de l{\textquoteright}adulte en Afrique sub-saharienne et au Maghreb]},
	journal = {Revue Francaise d{\textquoteright}Allergologie},
	volume = {60},
	number = {4},
	year = {2020},
	note = {cited By 0},
	pages = {297-299},
	abstract = {{Objective: To describe the demographic, clinical, paraclinical and therapeutic characteristics of patients with atopic dermatitis in Sub-Saharan Africa (SSA) and the Maghreb. Methods: The Medical Doctors members of the Federation Allergologie Internationale (FAI) working in SSA and the Maghreb were invited to participate in this cross-sectional study to consecutively including patients with atopic dermatitis (AD) seen in their consultations from September 2019 to January 2010 (5 months). Results: A total of 69 patients (53.6\% female) were included. The proportion of female subjects was higher among subjects included in SSA (69.7\% vs 38.9\%},
	keywords = {adolescent, adult, Africa south of the Sahara, allergic rhinitis, asthma, Atopic dermatitis, bacterial skin disease, child, clinical assessment, clinical feature, consultation, cross-sectional study, daytime somnolence, disease association, female, food allergen, human, major clinical study, male, prevalence, prurigo, seborrheic dermatitis, Short Survey},
	doi = {10.1016/j.reval.2020.02.014},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081410340\&doi=10.1016\%2fj.reval.2020.02.014\&partnerID=40\&md5=f42b72ed95d78155dedf2d2b24439201},
	author = {Pefura-Yone, E.W. and Jeddi, Z. and Kouotou, E.A. and Delimi, B. and El Gueddari, Y. and Karkar, R. and Kebe, M. and Diallo Chauvin, M. and Dumur, J.-P.}
}
