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Vincent Schilling, Peter Beyerlein and Jeremy Chien
The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients? outcomes an...
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Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Andreas Kanavos, Spyros Sioutas and Athanasios Tsakalidis
It has already been established by the systems-level approaches that the future of predictive disease biomarkers will not be sketched by plain lists of genes or proteins or other biological entities but rather integrated entities that consider all underl...
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Joaquim Carreras, Yara Yukie Kikuti, Masashi Miyaoka, Shinichiro Hiraiwa, Sakura Tomita, Haruka Ikoma, Yusuke Kondo, Atsushi Ito, Naoya Nakamura and Rifat Hamoudi
The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension...
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Brigitte Sipos, Peter Jirak, Vera Paar, Richard Rezar, Moritz Mirna, Kristen Kopp, Uta C. Hoppe, Alexander E. Berezin and Michael Lichtenauer
Cardiovascular diseases remain the most common causes of death globally, according to the World Health Organization. In recent years, a great number of biomarkers have been investigated, whereas only some have gained value in the diagnosis, prognosis, an...
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Edna J. Nava-Gonzalez, Esther C. Gallegos-Cabriales, Irene Leal-Berumen and Raul A. Bastarrachea
Studies of gene-environment (GxE) interactions describe how genetic and environmental factors influence the risk of developing disease. Intermediate (molecular or clinical) phenotypes (IPs) are traits or metabolic biomarkers that mediate the effects of g...
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