AI Medical Compendium Topic:
Models, Statistical

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Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning ...

Using classification tree analysis to generate propensity score weights.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: In evaluating non-randomized interventions, propensity scores (PS) estimate the probability of assignment to the treatment group given observed characteristics. Machine learning algorithms have been proposed as an alte...

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Proteins
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during t...

Multitask Protein Function Prediction through Task Dissimilarity.

IEEE/ACM transactions on computational biology and bioinformatics
Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning...

A Framework for Mixed-Type Multioutcome Prediction With Applications in Healthcare.

IEEE journal of biomedical and health informatics
Health analysis often involves prediction of multiple outcomes of mixed type. The existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multioutcome prediction. Our proposed framework...

Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems.

Journal of neural engineering
OBJECTIVE: This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandw...

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

BMC genomics
BACKGROUND: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by di...

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

BMC genomics
BACKGROUND: Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying patterns of pathway perturbations would provide insights into common disease mechanis...

Regularized Speaker Adaptation of KL-HMM for Dysarthric Speech Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
This paper addresses the problem of recognizing the speech uttered by patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. Patients with dysarthria have articulatory limitation, and therefore, they of...