AI Medical Compendium Topic:
Models, Biological

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Machine-learning-guided directed evolution for protein engineering.

Nature methods
Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the ...

AI- modelling of molecular identification and feminization of wolbachia infected Aedes aegypti.

Progress in biophysics and molecular biology
BACKGROUND: The genetic control strategies of vector borne diseases includes the replacement of a vector population by "disease-refractory" mosquitoes and the release of mosquitoes with a gene to control the vector's reproduction rates. Wolbachia are...

Complexity and entropy representation for machine component diagnostics.

PloS one
The Complexity-entropy causality plane (CECP) is a parsimonious representation space for time series. It has only two dimensions: normalized permutation entropy ([Formula: see text]) and Jensen-Shannon complexity ([Formula: see text]) of a time serie...

Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Proteins
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...

Cellular frustration algorithms for anomaly detection applications.

PloS one
Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emer...

Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production.

Bioprocess and biosystems engineering
An accurate and reliable forecast of biosurfactant production with minimum error is useful in any bioprocess engineering. Bacterial isolate FKOD36 capable of producing biosurfactant was isolated in this study and pre-inoculums was prepared from the a...

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases.

Sensors (Basel, Switzerland)
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on ho...

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Artificial intelligence in medicine
OBJECTIVES: The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans.

ALTEX
Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of on...

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Journal of chemical information and modeling
Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, th...