AI Medical Compendium Topic:
Models, Biological

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Advancing System Performance with Redundancy: From Biological to Artificial Designs.

Neural computation
Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended. Until recently, the only understanding of redundanc...

Machine learning framework for assessment of microbial factory performance.

PloS one
Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other han...

Enhancing Confusion Entropy (CEN) for binary and multiclass classification.

PloS one
Different performance measures are used to assess the behaviour, and to carry out the comparison, of classifiers in Machine Learning. Many measures have been defined on the literature, and among them, a measure inspired by Shannon's entropy named the...

Multi-task learning improves ancestral state reconstruction.

Theoretical population biology
We consider the ancestral state reconstruction problem where we need to infer phenotypes of ancestors using observations from present-day species. For this problem, we propose a multi-task learning method that uses regularized maximum likelihood to e...

Hindmarsh-Rose neuron model with memristors.

Bio Systems
We analyze single and coupled Hindmarsh-Rose neurons in the presence of a time varying electromagnetic field which results from the exchange of ions across the membrane. Memristors are used to model the relation between magnetic flux of the electroma...

Deep Learning for Musculoskeletal Force Prediction.

Annals of biomedical engineering
Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in ...

Deep learning for DNase I hypersensitive sites identification.

BMC genomics
BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources availab...

Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer.

Theoretical biology & medical modelling
BACKGROUND: The Smad7 protein is negative regulator of the TGF-β signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify...

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Journal of chemical information and modeling
Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the st...

Private naive bayes classification of personal biomedical data: Application in cancer data analysis.

Computers in biology and medicine
Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients' privacy. In addition, the medical institutions and companies who own these medical inform...