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pro vyhledávání: '"Bender Andreas"'
Three-dimensional (3D) deep molecular generative models offer the advantage of goal-directed generation based on 3D-dependent properties, such as binding affinity for structure-based design within binding pockets. Traditional benchmarks created to ev
Externí odkaz:
http://arxiv.org/abs/2407.04424
Autor:
Burk, Lukas, Zobolas, John, Bischl, Bernd, Bender, Andreas, Wright, Marvin N., Sonabend, Raphael
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing mo
Externí odkaz:
http://arxiv.org/abs/2406.04098
Autor:
Seal, Srijit, Trapotsi, Maria-Anna, Spjuth, Ola, Singh, Shantanu, Carreras-Puigvert, Jordi, Greene, Nigel, Bender, Andreas, Carpenter, Anne E.
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we syste
Externí odkaz:
http://arxiv.org/abs/2405.02767
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains. It is also an important example of probabilistic machine learning. The probabilistic nature of the predictions can be exploited by using (pro
Externí odkaz:
http://arxiv.org/abs/2403.13150
Autor:
Lawrence, Elsa, El-Shazly, Adham, Seal, Srijit, Joshi, Chaitanya K, Liò, Pietro, Singh, Shantanu, Bender, Andreas, Sormanni, Pietro, Greenig, Matthew
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in l
Externí odkaz:
http://arxiv.org/abs/2403.04106
Autor:
Hornung, Roman, Nalenz, Malte, Schneider, Lennart, Bender, Andreas, Bothmann, Ludwig, Bischl, Bernd, Augustin, Thomas, Boulesteix, Anne-Laure
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, particularly those where observations are not independently and identically
Externí odkaz:
http://arxiv.org/abs/2310.15108
Publikováno v:
Artif Intell Rev 57, 65 (2024)
The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In t
Externí odkaz:
http://arxiv.org/abs/2305.14961
Scoring rules promote rational and honest decision-making, which is becoming increasingly important for automated procedures in `auto-ML'. In this paper we survey common squared and logarithmic scoring rules for survival analysis and determine which
Externí odkaz:
http://arxiv.org/abs/2212.05260
De novo molecule generation can suffer from data inefficiency; requiring large amounts of training data or many sampled data points to conduct objective optimization. The latter is a particular disadvantage when combining deep generative models with
Externí odkaz:
http://arxiv.org/abs/2212.01385
Publikováno v:
Journal of Integrative Bioinformatics, Vol 9, Iss 1, Pp 44-71 (2012)
Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific o
Externí odkaz:
https://doaj.org/article/4b6717d1bcfe4fe287aeb5e382f31d8a