AIMC Topic: Likelihood Functions

Clear Filters Showing 71 to 80 of 89 articles

Prediction of hospitalization due to heart diseases by supervised learning methods.

International journal of medical informatics Oct 16, 2014
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that n...
Boston Heart Diseases Likelihood Functions Hospitalization Risk Assessment Logistic Models Bayes Theorem ROC Curve Algorithms Electronic Health Records Humans Artificial Intelligence
View on PubMed DOI

How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?

Pharmacoepidemiology and drug safety May 1, 2025
BACKGROUND: Residual confounding presents a persistent challenge in observational studies, particularly in high-dimensional settings. High-dimensional proxy adjustment methods, such as the high-dimensional propensity score (hdPS), are widely used to ...
Machine Learning Computer Simulation Logistic Models Humans Propensity Score Likelihood Functions Confounding Factors, Epidemiologic Observational Studies as Topic Bias
View on PubMed DOI

Phyloformer: Fast, Accurate, and Versatile Phylogenetic Reconstruction with Deep Neural Networks.

Molecular biology and evolution Apr 1, 2025
Phylogenetic inference aims at reconstructing the tree describing the evolution of a set of sequences descending from a common ancestor. The high computational cost of state-of-the-art maximum likelihood and Bayesian inference methods limits their us...
Sequence Alignment Phylogeny Deep Learning Evolution, Molecular Likelihood Functions Models, Genetic Neural Networks, Computer Software
View on PubMed DOI

Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.

Statistics in medicine Mar 15, 2025
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these mo...
Machine Learning Causality Models, Statistical Humans Proportional Hazards Models Likelihood Functions
View on PubMed DOI

An efficient deep learning method for amino acid substitution model selection.

Journal of evolutionary biology Jan 3, 2025
Amino acid substitution models play an important role in studying the evolutionary relationships among species from protein sequences. The amino acid substitution model consists of a large number of parameters; therefore, it is estimated from hundred...
Amino Acid Substitution Likelihood Functions Models, Genetic Evolution, Molecular Sequence Alignment Phylogeny Deep Learning Computer Simulation
View on PubMed DOI

A machine-learning-based alternative to phylogenetic bootstrap.

Bioinformatics (Oxford, England) Jun 28, 2024
MOTIVATION: Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic ana...
Likelihood Functions Sequence Alignment Phylogeny Computational Biology Software Algorithms Machine Learning
View on PubMed DOI

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Systematic biology May 27, 2024
Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computation...
Classification Likelihood Functions Phylogeography Viruses Phylogeny Deep Learning Bayes Theorem
View on PubMed DOI

Deep Learning from Phylogenies for Diversification Analyses.

Systematic biology Dec 30, 2023
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood ...
Animals Deep Learning Likelihood Functions Phylogeny Primates Genetic Speciation
View on PubMed DOI

Fusang: a framework for phylogenetic tree inference via deep learning.

Nucleic acids research Nov 10, 2023
Phylogenetic tree inference is a classic fundamental task in evolutionary biology that entails inferring the evolutionary relationship of targets based on multiple sequence alignment (MSA). Maximum likelihood (ML) and Bayesian inference (BI) methods ...
Sequence Alignment Bayes Theorem Likelihood Functions Phylogeny Deep Learning Algorithms
View on PubMed DOI

Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.

Genome biology and evolution Jun 1, 2023
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implem...
Machine Learning Models, Statistical Likelihood Functions Algorithms Phylogeny
View on PubMed DOI
Popular Topics
Recent Journals