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Grafi struktur proteinov
Miloš Tomić, 2017, diplomsko delo

Najdeno v: osebi
Ključne besede: proteins, amino acids, graph theory, protein graphs, radius of a graph, average shortest path, average node degree, graph energy
Objavljeno: 09.11.2017; Ogledov: 1529; Prenosov: 26
URL Polno besedilo (0,00 KB)

Scaling laws of graphs of 3D protein structures
Jure Pražnikar, 2021, izvirni znanstveni članek

Najdeno v: osebi
Ključne besede: graph theory, scaling law, macromolecules, radius of gyration, eccentricity
Objavljeno: 04.02.2021; Ogledov: 328; Prenosov: 23
URL Polno besedilo (0,00 KB)

Miha Peroša, Nejc Šarabon, Jure Pražnikar, Helmut Kern, 2015, objavljeni povzetek znanstvenega prispevka na konferenci

Najdeno v: osebi
Ključne besede: measurements, mobile technology, physiology, ergonomics
Objavljeno: 15.10.2015; Ogledov: 2288; Prenosov: 24
URL Polno besedilo (0,00 KB)

Particulate matter (PM10) patterns in Europe
Jure Cedilnik, Janez Žibert, Jure Pražnikar, 2016, izvirni znanstveni članek

Najdeno v: osebi
Ključne besede: particular matter, non-negative matrix factorization, space-time patterns, synoptic situations
Objavljeno: 08.08.2016; Ogledov: 2024; Prenosov: 115
URL Polno besedilo (0,00 KB)

Validation and quality assessment of macromolecular structures using complex network analysis
Miloš Tomić, Dušan Turk, Jure Pražnikar, 2019, izvirni znanstveni članek

Najdeno v: osebi
Ključne besede: validation, complex network, model quality, proteins
Objavljeno: 13.02.2019; Ogledov: 1204; Prenosov: 236
URL Polno besedilo (0,00 KB)

Obesity measures and dietary parameters as predictors of gut microbiota phyla in healthy individuals
Marcos Ascensión, Noemi Redondo, Esther Nova, Zala Jenko Pražnikar, Jure Pražnikar, Ana Petelin, Katja Bezek, 2020, izvirni znanstveni članek

Opis: : The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host%s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25%50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition.
Najdeno v: osebi
Ključne besede: gut microbiota, nutrition, obesity, lifestyle parameters, clustering
Objavljeno: 10.09.2020; Ogledov: 542; Prenosov: 49
URL Polno besedilo (0,00 KB)

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