AIMC Topic: Likelihood Functions

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Phylogeny analysis from gene-order data with massive duplications.

BMC genomics
BACKGROUND: Gene order changes, under rearrangements, insertions, deletions and duplications, have been used as a new type of data source for phylogenetic reconstruction. Because these changes are rare compared to sequence mutations, they allow the i...

A machine learning approach for predicting methionine oxidation sites.

BMC bioinformatics
BACKGROUND: The oxidation of protein-bound methionine to form methionine sulfoxide, has traditionally been regarded as an oxidative damage. However, recent evidences support the view of this reversible reaction as a regulatory post-translational modi...

Vascular tree tracking and bifurcation points detection in retinal images using a hierarchical probabilistic model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network...

An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
OBJECTIVE: This study aims to evaluate the development of an artificial neural network (ANN) method for predicting the survival likelihood of pancreatic adenocarcinoma patients. The ANN predictive model should produce results with a 90% sensitivity.

Maximum likelihood optimal and robust Support Vector Regression with lncosh loss function.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel and continuously differentiable convex loss function based on natural logarithm of hyperbolic cosine function, namely lncosh loss, is introduced to obtain Support Vector Regression (SVR) models which are optimal in the maximum ...

Graph-based composite local Bregman divergences on discrete sample spaces.

Neural networks : the official journal of the International Neural Network Society
This paper develops a general framework of statistical inference on discrete sample spaces, on which a neighborhood system is defined by an undirected graph. The scoring rule is a measure of the goodness of fit for the model to observed samples, and ...

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PloS one
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm ...

Online cross-validation-based ensemble learning.

Statistics in medicine
Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinit...

Prediction of virus-host infectious association by supervised learning methods.

BMC bioinformatics
BACKGROUND: The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing...

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

American journal of epidemiology
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood esti...