Multidimensional Scaling for Genomic Data

By A. Jakaitienė, Mara Sangiovanni, M. Guarracino, P. Pardalos. 2016

7 citations recorded by the source corpus.

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Mathematical optimization in classification and regression trees (2021, background): “Extracting knowledge from data is a crucial task in Statistics and Machine Learning, and is at the core of many fields, such as Biomedicine (Jakaitiene et al. 2016; Pardalos et al. 2007); Business Analytics (Martens et al. 2007; Van Vlasselaer et al. 2017), Computational Optimization (Khalil et al.…”
Optimal randomized classification trees (2021, neutral): “Extracting knowledge from data is a crucial task in Statistics and Machine Learning, which has applications in areas such as Biomedicine [1, 2], Criminal Justice [3, 4, 5], Fraud Detection [6], Privacy Protection [7], Health Care [8, 9, 10, 11], Risk Management [12, 13], Social Networks [14], and Co”
The trouble with stress: A flexible method for the evaluation of nonmetric multidimensional scaling (2018, background): “Similar issues have arisen in the application of NMDS to genomic data, as modern genomic studies may contain thousands of individual markers examined across highly dimensional (k>10) ordinations (Tzeng et al. 2008; Jakaitiene et al. 2016).”