Ponedeljkov seminar računalništva in informatike - Arhiv

petek, 27. marec 2020 András BÓTA: Network-based framework for estimating the risk factors of epidemic spreading

V ponedeljek, 30. marca 2020, bo ob 16.00 uri prek spletnih orodij na daljavo izvedeno predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 30. marec 2020 ob 16.00 na daljavo

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PREDAVATELJ: András BÓTA
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András Bóta finished his postgraduate studies in Computer Science at the University of Szeged under the supervision of Dr. Miklós Krész and Dr. András Pluhár in 2015, then worked as a Research Associate at the University of New South Wales with Dr. Lauren Gardner for two years. Following that, he worked as a postdoctoral fellow at Umeå University. His main research interests lie in the fields of complex network analysis and artificial intelligence.

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NASLOV: Network-based framework for estimating the risk factors of epidemic spreading
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POVZETEK:

Network-based adaptations of traditional compartmental infection models such as SIR or SEIR can be used to model the spreading of diseases between cities, countries or other geographical regions. One of the common challenges arising in these applications is the lack of available transmission probabilities between these geographical units. The task of inverse infection is the systematic estimation of these values. Several methods have been proposed recently for solving this task. One of them is the Generalized Inverse Infection Model (GIIM) [1]. GIIM offers a large amount of modeling flexibility and allows transmission probabilities to be defined as a function of known attributes, or risk factors in an epidemic context. In this presentation we will see how GIIM works in two specific real-life outbreaks.
Both examples are embedded in a geographical and temporal setting. The first one considers the 2015-2016 Zika virus outbreak in the Americas, where the countries and overseas territories of the continent form the nodes of the network and air travel routes define the links [2]. The second application models the 2009 H1N1 outbreak between the municipalities of Sweden, with links between the municipalities indicating frequent travel routes.
Our first goal in both of these studies is to discover the relationship between the transmission risk between geographical units and a variety of travel, environmental, meteorological and socioeconomic risk factors. Our second goal is to estimate the risk of exportation and importation of the diseases for the territories involved in these studies. We will show that the GIIM model is able to identify the most critical risk factors in both scenarios, and in the influenza study, it is able make predictions about future outbreaks with good accuracy.

REFERENCE:
1. A. Bóta, L. M. Gardner: A generalized framework for the estimation of edge infection probabilities. arXiv:1706.07532 (2017)
2. L. M. Gardner, A. Bóta, N. D. Grubaugh, K. Gangavarapu, M. U. G. Kramer: Inferring the risk factors behind the geographical spread and transmission of Zika in the Americas.PLoS Neglected Tropical Diseases. http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0006194

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Predavanje bo potekalo v angleškem jeziku prek spletnega orodja Zoom.

Do predavanja dostopate tako, da se povežete prek sledeče povezave: https://zoom.us/j/297328207

Vabljeni! 

sobota, 14. marec 2020 Jordan Aiko DEJA: Using EEG for Emotion Experience Design in TV Commercials

petek, 6. marec 2020 Aleš MAVER: Big data and the human genome

V ponedeljek, 9. marca 2020, bo ob 16.00 uri v prostorih Fakultete za matematiko, naravoslovje in informacijske tehnologije Univerze na Primorskem, Glagoljaška 8, Koper predavanje v okviru PONEDELJKOVEGA SEMINARJA RAČUNALNIŠTVA IN INFORMATIKE Oddelkov za Informacijske znanosti in tehnologije UP FAMNIT in UP IAM.

ČAS/PROSTOR: 9. marec 2020 ob 16.00 v FAMNIT-VP2

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PREDAVATELJ: dr. Aleš MAVER

Dr. Aleš Maver has been working in the field of human genetics since 2002, and in his work he focuses on research of complex diseases, bioinformatics and diagnostics of rare genetic disorders. After graduating from medical school in 2011, he continued with his PhD work on rare genetic variations in familial multiple sclerosis using sequence analysis of the complete set of genes in the human genome (exome sequencing). Since then, he's been dealing with the use of big genomic data to diagnose patients with rare diseases. To that end, in 2013, he participated in the establishment of the Centre for Mendelian Genomics, which now functions as a center that offers state-of-the-art technology and bioinformatics to diagnose genetic diseases and is involved in global genome-wide sequencing data initiatives. Today his main interests include identifying new genes for genetic diseases, improving the interpretation of genetic variants at the genomic level and improving the exchange of genomic data in Slovenia and Europe.

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NASLOV: Big data and the human genome

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POVZETEK:

New genomic technologies have, in the recent years, provided unimaginable opportunities for human genome investigations, and thus significant advances in disease diagnosis and the discovery of new genes and disease mechanisms. With new approaches, especially through next-generation sequencing, sequencing of the entire human genome has become significantly cheaper, faster and more accessible. At the same time, we require novel bioinformatic approaches and significantly greater computational capacity to process the large amounts of data generated using novel genetic technologies. In my talk, I would like to present how we are facing the new challenges of big data in the time of genomic medicine, how we use big data of genomic sequencing in the discovery of new genes for human hereditary diseases, and how the analysis of such data has become the basis of modern genetic diagnostics in Slovenia today.

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Predavanje bo potekalo v angleškem jeziku.

Vabljeni!