Search Results for author: Andreas Mayr

Found 19 papers, 12 papers with code

Redundancy-aware unsupervised rankings for collections of gene sets

no code implementations30 Jul 2023 Chiara Balestra, Carlo Maj, Emmanuel Müller, Andreas Mayr

The rankings can be used to reduce the dimension of collections of gene sets, such that they show lower redundancy and still a high coverage of the genes.

Redundancy-aware unsupervised ranking based on game theory -- application to gene enrichment analysis

1 code implementation22 Jul 2022 Chiara Balestra, Carlo Maj, Emmanuel Mueller, Andreas Mayr

However, we believe that the rankings proposed are of use in bioinformatics to increase interpretability of the gene sets collections and a step forward to include redundancy into Shapley values computations.

Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

1 code implementation17 May 2022 Chiara Balestra, Florian Huber, Andreas Mayr, Emmanuel Müller

Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.

Anomaly Detection Feature Importance +1

Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts

no code implementations6 Sep 2021 Tobias Wistuba, Andreas Mayr, Christian Staerk

This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model.

Boundary Graph Neural Networks for 3D Simulations

1 code implementation21 Jun 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

However, it is notoriously difficult to integrate them into machine learning approaches due to their heterogeneity with respect to size and orientation.

Learning 3D Granular Flow Simulations

1 code implementation4 May 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields.

BIG-bench Machine Learning

Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany

no code implementations4 Nov 2020 Christian Staerk, Tobias Wistuba, Andreas Mayr

Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections.

Cross-Domain Few-Shot Learning by Representation Fusion

2 code implementations13 Oct 2020 Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, Sepp Hochreiter

On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.

cross-domain few-shot learning Drug Discovery

Probing for sparse and fast variable selection with model-based boosting

no code implementations15 Feb 2017 Janek Thomas, Tobias Hepp, Andreas Mayr, Bernd Bischl

We present a new variable selection method based on model-based gradient boosting and randomly permuted variables.

Variable Selection

Stability selection for component-wise gradient boosting in multiple dimensions

1 code implementation30 Nov 2016 Janek Thomas, Andreas Mayr, Bernd Bischl, Matthias Schmid, Adam Smith, Benjamin Hofner

We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures.

Additive models

Boosting Joint Models for Longitudinal and Time-to-Event Data

1 code implementation9 Sep 2016 Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein, Thomas Kneib, Tania Pressler, Matthias Schmid, Andreas Mayr

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times.

Variable Selection

Toxicity Prediction using Deep Learning

1 code implementation4 Mar 2015 Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Sepp Hochreiter

The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds.

The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling

1 code implementation6 Mar 2014 Andreas Mayr, Harald Binder, Olaf Gefeller, Matthias Schmid

This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling.


Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

no code implementations24 Jul 2013 Andreas Mayr, Matthias Schmid

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics.

feature selection

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