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Query: "keywords" (clustering) .

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1.
Faster distance-based representative skyline and k-center along pareto front in the plane
Sergio Cabello, 2023, original scientific article

Abstract: We consider the problem of computing the distance-based representative skyline in the plane, a problem introduced by Tao, Ding, Lin and Pei and independently considered by Dupin, Nielsen and Talbi in the context of multi-objective optimization. Given a set $P$ of $n$ points in the plane and a parameter $k$, the task is to select $k$ points of the skyline defined by $P$ (also known as Pareto front for $P$) to minimize the maximum distance from the points of the skyline to the selected points. We show that the problem can be solved in $O(n \log h)$ time, where $h$ is the number of points in the skyline of $P$. We also show that the decision problem can be solved in $O(n \log k)$ time and the optimization problem can be solved in $O(n \log k + n \log\log n)$ time. This improves previous algorithms and is optimal for a large range of values of $k$.
Keywords: geometric optimization, skyline, pareto front, clustering, k-center
Published in DiRROS: 15.03.2024; Views: 49; Downloads: 30
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2.
Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children
Ivana Banić, Mario Lovrić, Gerald Cuder, Roman Kern, Matija Rijavec, Peter Korošec, Mirjana Kljajić-Turkalj, 2021, original scientific article

Abstract: Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N = 58) and 3 (N = 138) had better treatment outcomes compared to clusters 2 and 4 and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6 + yrs). Clusters 2 (N = 87) and 4 (N = 64) had poorer treatment success, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2), level of systemic inflammation (highest hsCRP for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes.
Keywords: asthma, allergy and immunology, pediatrics, machine learning, treatment outcome, phenotypes, childhood asthma, clustering
Published in DiRROS: 16.08.2021; Views: 890; Downloads: 631
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3.
Triangular in vivo self-assembling coiled-coil protein origami
Sabina Božič Abram, Helena Gradišar, Jana Aupič, Adam Round, Roman Jerala, 2021, original scientific article

Keywords: coiled coil, enzyme clustering, biosynthesis, resveratrol, mevalonate
Published in DiRROS: 22.03.2021; Views: 1406; Downloads: 888
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