Search Results for author:
Found 23 papers, 15 papers with code
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences.
Ranking evaluation metrics from a group-theoretic perspective
Firstly, we show instances resulting in inconsistent evaluations, sources of potential mistrust in commonly used metrics; by quantifying the frequency of such disagreements, we prove that these are common in rankings.
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
However, the performance of GNNs at binding site identification is still limited potentially due to the lack of dedicated nodes that model hidden geometric entities, such as binding pockets.
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks
Graph neural networks (GNNs), and especially message-passing neural networks, excel in various domains such as physics, drug discovery, and molecular modeling.
Redundancy-aware unsupervised rankings for collections of gene sets
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
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
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.
Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts
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
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
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.
Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany
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
On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Self-Normalizing Neural Networks
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Ranked #8 on
Drug Discovery
on Tox21
An update on statistical boosting in biomedicine
Statistical boosting algorithms have triggered a lot of research during the last decade.
Probing for sparse and fast variable selection with model-based boosting
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables.
Stability selection for component-wise gradient boosting in multiple dimensions
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.
Boosting Joint Models for Longitudinal and Time-to-Event Data
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.
Speeding up Semantic Segmentation for Autonomous Driving
We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
Toxicity Prediction using Deep Learning
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
This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling.
Methodology
Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations
The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics.
