AI Medical Compendium Journal:
Mathematical biosciences

Showing 1 to 10 of 20 articles

Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks.

Mathematical biosciences
The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction-diffusion processes which model such systems is highly computationally expensive, the cost ...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Mathematical biosciences
Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficient anticoagulant and the appropriate dosage regimen remain a complex problem because of the intersubj...

The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.

Mathematical biosciences
One way to interject knowledge into clinically impactful forecasting is to use data assimilation, a nonlinear regression that projects data onto a mechanistic physiologic model, instead of a set of functions, such as neural networks. Such regressions...

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.

Mathematical biosciences
A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play signif...

Termite population size estimation based on termite tunnel patterns using a convolutional neural network.

Mathematical biosciences
Subterranean termites live in large colonies under the ground where they build an elaborate network of tunnels for foraging. In this study, we explored how the termite population size can be estimated using partial information on tunnel patterns. To ...

Conservation region finding for influenza A viruses by machine learning methods of N-linked glycosylation sites and B-cell epitopes.

Mathematical biosciences
Influenza type A, a serious infectious disease of the human respiratory tract, poses an enormous threat to human health worldwide. It leads to high mortality rates in poultry, pigs, and humans. The primary target identity regions for the human immune...

Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

Mathematical biosciences
It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miR...

A novel conjoint triad auto covariance (CTAC) coding method for predicting protein-protein interaction based on amino acid sequence.

Mathematical biosciences
Protein-protein interactions (PPIs) play a crucial role in the life-sustaining activities of organisms. Although various methods for the prediction of PPIs have been developed in the past decades, their robustness and prediction accuracy need to be i...

Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier.

Mathematical biosciences
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore...