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Found 21 papers, 8 papers with code
Training Survival Models using Scoring Rules
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains.
Understanding Biology in the Age of Artificial Intelligence
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.
Evaluating machine learning models in non-standard settings: An overview and new findings
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
Deep Learning for Survival Analysis: A Review
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.
Conditional Neural Processes for Molecules
Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs).
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests
Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects.
Factorized Structured Regression for Large-Scale Varying Coefficient Models
Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models.
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications.
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination.
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explosion of novel methods.
A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
mlr3proba: An R Package for Machine Learning in Survival Analysis
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models.
A General Machine Learning Framework for Survival Analysis
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events.
Concepts and Applications of Conformal Prediction in Computational Drug Discovery
Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings.
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Here, we present a framework to compute reliable errors in prediction for Neural Networks using Test-Time Dropout and Conformal Prediction.
KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints.
Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks
While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques to estimate the reliability of the predictions generated with deep learning networks remain largely underexplored.
pammtools: Piece-wise exponential Additive Mixed Modeling tools
This article introduces the pammtools package, which facilitates data transformation, estimation and interpretation of Piece-wise exponential Additive Mixed Models.
Computation
DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning
While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space.
Target Fishing: A Single-Label or Multi-Label Problem?
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable.
