AI Medical Compendium Journal:
NPJ systems biology and applications

Showing 11 to 20 of 30 articles

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

NPJ systems biology and applications
Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks inform...

Computational gastronomy: capturing culinary creativity by making food computable.

NPJ systems biology and applications
Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culina...

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

NPJ systems biology and applications
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.

NPJ systems biology and applications
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Com...

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.

NPJ systems biology and applications
Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Esche...

AutoTransOP: translating omics signatures without orthologue requirements using deep learning.

NPJ systems biology and applications
The development of therapeutics and vaccines for human diseases requires a systematic understanding of human biology. Although animal and in vitro culture models can elucidate some disease mechanisms, they typically fail to adequately recapitulate hu...

Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE.

NPJ systems biology and applications
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE...

Reliable interpretability of biology-inspired deep neural networks.

NPJ systems biology and applications
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where re...

Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.

NPJ systems biology and applications
Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. ...

A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape.

NPJ systems biology and applications
Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, wh...