AI Medical Compendium Topic:
Models, Biological

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Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning.

EBioMedicine
Clinical prediction of advanced hepatic fibrosis (HF) and cirrhosis has long been challenging due to the gold standard, liver biopsy, being an invasive approach with certain limitations. Less invasive blood test tandem with a cutting-edge machine lea...

A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

Journal of chemical information and modeling
HIV resistance emerging against antiretroviral drugs represents a great threat to the continued prolongation of the lifespans of HIV-infected patients. Therefore, methods capable of predicting resistance susceptibility in the development of compounds...

Reinforcement learning for solution updating in Artificial Bee Colony.

PloS one
In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimens...

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...

On the use of machine learning techniques for the mechanical characterization of soft biological tissues.

International journal for numerical methods in biomedical engineering
Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, ...

Enabling Precision Medicine through Integrative Network Models.

Journal of molecular biology
A key challenge in precision medicine lies in understanding molecular-level underpinnings of complex human disease. Biological networks in multicellular organisms can generate hypotheses about disease genes, pathways, and their behavior in disease-re...

Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks: an application to a controlled clinical trial on renal carcinoma.

BMC bioinformatics
BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of ...

Soft Biomimetic Fish Robot Made of Dielectric Elastomer Actuators.

Soft robotics
This article presents the design, fabrication, and characterization of a soft biomimetic robotic fish based on dielectric elastomer actuators (DEAs) that swims by body and/or caudal fin (BCF) propulsion. BCF is a promising locomotion mechanism that p...

Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma.

Computers in biology and medicine
Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop ...

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...