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Wolf Beat

Assistenzprofessor FH

Hauptkompetenzen

Distributed and Parallel Computing

Assistenzprofessor FH

Büro: HEIA_D20.15

Haute école d'ingénierie et d'architecture de Fribourg
Boulevard de Pérolles 80, 1700 Fribourg, CH

Institut
iCoSys - Institut des systèmes complexes

BA HES-SO en Architecture - Haute école d'ingénierie et d'architecture de Fribourg

Programmation C/C++
Mathématiques spécifiques 2
Traitement de données
Machine Learning

MSc HES-SO en Business Administration - HES-SO Master

Introduction to Business Intelligence

Laufend

Optimization of support system for urban deep excavations with machine learning techniques

Rolle: Mitarbeiter

Financement: Hasler; FR - EIA - Institut iTEC; EPFL ENAC LMS

Description du projet : Productivity in the construction sector, particularly in soil excavation, has stagnated for decades. The main advances were made 30 years ago with the development of finite element simulation tools. Today, planning an excavation for a building is still based on a long human process of trial and error. At the same time, important steps are being taken to unify data models for different construction professions. Important progresses are also being made in the field of soil cartography in Switzerland, such as SwissTopo. The development of these models will provide large amounts of numerical data describing the different parameters and stages of building construction and in particular excavation in a given soil environment. The underlying scientific question is about the usability of such data to improve methods and knowledge about geotechnical structure design. More specifically, the objective of the OptiSoil project is to use this data to apply artificial intelligence algorithms using machine learning techniques to develop innovative excavation design techniques that optimize stability, risk and cost constraints.

Forschungsteam innerhalb von HES-SO: Linder Lucy, Hennebert Jean, Kuonen Pierre, Commend Stéphane, Donzallaz Jonathan, Menoud Ludivine, Buchs Colette, Wolf Beat

Partenaires académiques: FR - EIA - Institut iCoSys

Durée du projet: 06.06.2019 - 31.01.2024

Montant global du projet: 49'385 CHF

Statut: Laufend

Abgeschlossen

Next Generation DNA Sequencing Cloud based platform

Rolle: Mitarbeiter

Requérant(e)s: FR - EIA - Institut iCoSys

Financement: CTI

Description du projet : Phenosystems SA has developed GensearchNGS, a framework geared towards diagnostics laboratories which analyses Next Generation Sequencing (NGS) data to detect changes in the DNA sequences for the diagnostic of genetic diseases.'Our current software distribution model is based on installation of the software at the customers' premises, requiring from them adequate computing power and storage capacity. Potential new customers are thus faced with a requirement for investment on the software and most of the time on the hardware side as well. This creates a barrier for them to start working with NGS data and might give them the opportunity to try out analytical services offered by competitors.'The GRID & Cloud Computing Group.of the HES-SO//Fribourg have been involved since numerous years in the development of applications and tools for large distributed systems. They have developed a model, and the associated software tools, for easy object oriented programming of distributed infrastructures such a clusters, Grids or Clouds. Two programming tools, called POP-C++ and POP-Java, have been developed. Both tools are based on an original distributed object oriented programming model called POP for: Parallel Object Programming. POP-C++ has already been intensively used in several research contexts.'The aim of this project is to study the feasiblity to offer our users GensearchNGS the possibility to run, time and memory consuming parts of the framework, on Cloud environments of their choice (our customers are based in various countries, and thus various legal constraints will require us to run on various Clouds).

Forschungsteam innerhalb von HES-SO: Goetschi Damien, Hennebert Jean, Rial Jonathan, Kuonen Pierre, Wolf Beat

Partenaires académiques: Phenosystem SA; FR - EIA - Institut iCoSys

Durée du projet: 01.11.2013 - 06.03.2021

Statut: Abgeschlossen

innosuisse ADVANCE - 31933.1 IP-ICT

Rolle: Mitarbeiter

Requérant(e)s: Hennebert Jean, FR - HEIA - Institut iCoSys

Financement: CTI; CM Profiling

Description du projet : ADVANCe: Augmented dialogue tool based on verbal and non-verbal behavior computing

Forschungsteam innerhalb von HES-SO: Hennebert Jean, Esseiva Julien, Zayene Oussama, Wolf Beat

Partenaires académiques: Gatica-Perez Daniel, IDIAP; Hennebert Jean, FR - HEIA - Institut iCoSys

Durée du projet: 27.05.2019 - 31.07.2020

Statut: Abgeschlossen

2021

A sparse observation model to quantify species distributions and their overlap in space and time

Scientifique ArODES

Ait Kaci Azzou Sadoune, Singer Liam, Aebischer Thierry, Caduff Madleina, Wolf Beat, Wegmann Daniel

Ecography, 2021, vol. 44, pp. 1-13

Link zur Publikation

Zusammenfassung:

Camera traps and acoustic recording devices are essential tools to quantify the distribution, abundance and behavior of mobile species. Varying detection probabilities among device locations must be accounted for when analyzing such data, which is generally done using occupancy models. We introduce a Bayesian time‐dependent observation model for camera trap data (Tomcat), suited to estimate relative event densities in space and time. Tomcat allows to learn about the environmental requirements and daily activity patterns of species while accounting for imperfect detection. It further implements a sparse model that deals well will a large number of potentially highly correlated environmental variables. By integrating both spatial and temporal information, we extend the notation of overlap coefficient between species to time and space to study niche partitioning. We illustrate the power of Tomcat through an application to camera trap data of eight sympatrically occurring duiker Cephalophinae species in the savanna – rainforest ecotone in the Central African Republic and show that most species pairs show little overlap. Exceptions are those for which one species is very rare, likely as a result of direct competition.

2020

Detecting selection from linked sites using an F-model

Scientifique ArODES

Galimberti Marco, Leuenberger Christoph, Wolf Beat, Szilagyi Sandor M., Foll Matthieu, Wegmann Daniel

Genetics, 2020, vol. 216, no. 2

Link zur Publikation

Zusammenfassung:

Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F-model, which measures differences in allele frequencies by population specific FST coefficients. This model readily accounts for multiple evolutionary forces by partitioning FST coefficients into locus and population specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations we show that the statistical power of our method is up to two-fold that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).

Using CNNs to optimize numerical simulations in geotechnical engineering

Scientifique

Beat Wolf, Jonathan Donzallaz, Colette Jost, Amanda Hayoz, Stéphane Commend, Jean Hennebert

Proceedings of IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020 : Artificial Neural Networks in Pattern Recognition, 2-4 September 2020, Winterthur, Switzerland, 2020

Link zur Publikation

Zusammenfassung:

Deep excavations are today mainly designed by manually optimising the wall’s geometry, stiffness and strut or anchor layout. In order to better automate this process for sustained excavations, we are exploring the possibility of approximating key values using a machine learning (ML) model instead of calculating them with time-consuming numerical simulations. After demonstrating in our previous work that this approach works for simple use cases, we show in this paper that this method can be enhanced to adapt to complex real-world supported excavations. We have improved our ML model compared to our previous work by using a convolutional neural network (CNN) model, coding the excavation configuration as a set of layers of fixed height containing the soil parameters as well as the geometry of the walls and struts. The system is trained and evaluated on a set of synthetically generated situations using numerical simulation software. To validate this approach, we also compare our results to a set of 15 real-world situations in a t-SNE. Using our improved CNN model we could show that applying machine learning to predict the output of numerical simulation in the domain of geotechnical engineering not only works in simple cases but also in more complex, real-world situations.

2019

A comprehensive method protocol for annotation and integrated functional understanding of lncRNAs

Scientifique ArODES

Kunz Meik, Wolf Beat, Fuchs Maximilian, Christoph Jan, Xiao Ke, Thum Thomas, Atlan David, Prokosch Hans-Ulrich, Dandekar Thomas

Briefings in Bioinformatics,

Link zur Publikation

Zusammenfassung:

Long non-coding RNAs (lncRNAs) are of fundamental biological importance; however, their functional role is often unclear or loosely defined as experimental characterization is challenging and bioinformatic methods are limited. We developed a novel integrated method protocol for the annotation and detailed functional characterization of lncRNAs within the genome. It combines annotation, normalization and gene expression with sequence-structure conservation, functional interactome and promoter analysis. Our protocol allows an analysis based on the tissue and biological context, and is powerful in functional characterization of experimental and clinical RNA-Seq datasets including existing lncRNAs. This is demonstrated on the uncharacterized lncRNA GATA6-AS1 in dilated cardiomyopathy.

2018

Single CpG hypermethylation, allele methylation errors, and decreased expression of multiple tumor suppressor genes in normal body cells of mutation-negative early-onset and high-risk breast cancer patients

Scientifique ArODES

Böck Julia, Appenzeller Silke, Haertle Larissa, Schneider Tamara, Gehrig Andrea, Schröder Jörg, Rost Simone, Wolf Beat, Bartram Claus R., Sutter Christian, Haaf Thomas

International Journal of Cancer, 2018, vol. 143, no. 6, pp. 1416-1425

Link zur Publikation

Zusammenfassung:

To evaluate the role of constitutive epigenetic changes in normal body cells of BRCA1/BRCA2‐mutation negative patients, we have developed a deep bisulfite sequencing assay targeting the promoter regions of 8 tumor suppressor (TS) genes (BRCA1, BRCA2, RAD51C, ATM, PTEN, TP53, MLH1, RB1) and the estrogene receptor gene (ESR1), which plays a role in tumor progression. We analyzed blood samples of two breast cancer (BC) cohorts with early onset (EO) and high risk (HR) for a heterozygous mutation, respectively, along with age‐matched controls. Methylation analysis of up to 50,000 individual DNA molecules per gene and sample allowed quantification of epimutations (alleles with >50% methylated CpGs), which are associated with epigenetic silencing. Compared to ESR1, which is representative for an average promoter, TS genes were characterized by a very low (< 1%) average methylation level and a very low mean epimutation rate (EMR; < 0.0001% to 0.1%). With exception of BRCA1, which showed an increased EMR in BC (0.31% vs. 0.06%), there was no significant difference between patients and controls. One of 36 HR BC patients exhibited a dramatically increased EMR (14.7%) in BRCA1, consistent with a disease‐causing epimutation. Approximately one third (15 of 44) EO BC patients exhibited increased rates of single CpG methylation errors in multiple TS genes. Both EO and HR BC patients exhibited global underexpression of blood TS genes. We propose that epigenetic abnormalities in normal body cells are indicative of disturbed mechanisms for maintaining low methylation and appropriate expression levels and may be associated with an increased BC risk.

2017

Reducing the complexity of OMICS data analysis

Thèses

Beat Wolf

2017, Würzburg, Germany : University of Würzburg

Link zur Publikation

2016

Non-coding RNAs in lung cancer :

Scientifique ArODES

contribution of bioinformatics analysis to the development of non-invasive diagnostic tools

Kunz Meik, Wolf Beat, Schulze Harald, Atlan David, Walles Thorsten, Walles Heike, Dandekar Thomas

Genes, 2017, vol. 8, no. 1, article no. 8

Link zur Publikation

Zusammenfassung:

Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.

Non-coding RNAs in lung cancer: Contribution of bioinformatics analysis to the development of non-invasive diagnostic tools

Scientifique

Meik Kunz, Beat Wolf

Human Genetics and Genomics, 2016

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2015

DNAseq workflow in a diagnostic context and an example of a user friendly implementation

Scientifique ArODES

Wolf Beat, Kuonen Pierre, Dandekar Thomas, Atlan David

BioMed Research International, 2015, article no. 403497

Link zur Publikation

Zusammenfassung:

Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.

2020

Using CNNs to optimize numerical simulations in geotechnical engineering

Conference ArODES

Jean Hennebert, Amanda Hayoz, Stéphane Commend, Beat Wolf, Jonathan Donzallaz

Lecture Notes in Computer Science ; Proceedings of IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020 : Artificial Neural Networks in Pattern Recognition, 2-4 September 2020, Winterthur, Switzerland

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Zusammenfassung:

Errungenschaften

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Wolf Beat

Assistenzprofessor FH

Hauptkompetenzen

Distributed and Parallel Computing

Programmation

Bio-Informatique

Machine Learning

Document Analysis

Data Analysis

Anomaly Detection

Assistenzprofessor FH