AI Medical Compendium Topic:
Models, Statistical

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Maximum margin semi-supervised learning with irrelevant data.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of...

Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration.

Biometrics
Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although, kernel-based st...

Towards a Food Safety Knowledge Base Applicable in Crisis Situations and Beyond.

BioMed research international
In case of contamination in the food chain, fast action is required in order to reduce the numbers of affected people. In such situations, being able to predict the fate of agents in foods would help risk assessors and decision makers in assessing th...

Weakly Supervised Human Fixations Prediction.

IEEE transactions on cybernetics
Automatically predicting human eye fixations is a useful technique that can facilitate many multimedia applications, e.g., image retrieval, action recognition, and photo retargeting. Conventional approaches are frustrated by two drawbacks. First, psy...

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

BMC bioinformatics
BACKGROUND: Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides in...

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.

Medical image analysis
Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that cont...

A roadmap to multifactor dimensionality reduction methods.

Briefings in bioinformatics
Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statisti...

Video-tracking of zebrafish (Danio rerio) as a biological early warning system using two distinct artificial neural networks: Probabilistic neural network (PNN) and self-organizing map (SOM).

Aquatic toxicology (Amsterdam, Netherlands)
Biological early warning systems (BEWS) are becoming very important tools in ecotoxicological studies because they can detect changes in the behavior of organisms exposed to toxic substances. In this work, a video tracking system was fully developed ...

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets.

Journal of chemical information and modeling
To date, no systematic study has assessed the effect of random experimental errors on the predictive power of QSAR models. To address this shortage, we have benchmarked the noise sensitivity of 12 learning algorithms on 12 data sets (15,840 models in...

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

PloS one
Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability...