AIMC Topic: Models, Biological

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Modeling polypharmacy side effects with graph convolutional networks.

Bioinformatics (Oxford, England)
MOTIVATION: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Po...

Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.

Optics letters
Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical...

O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique.

Bioinformatics (Oxford, England)
MOTIVATION: Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation c...

Reverse-engineering ecological theory from data.

Proceedings. Biological sciences
Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. co...

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

Biology letters
Ninety per cent of the world's data have been generated in the last 5 years ( Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by ...

Semi-supervised network inference using simulated gene expression dynamics.

Bioinformatics (Oxford, England)
MOTIVATION: Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regu...

A probabilistic pathway score (PROPS) for classification with applications to inflammatory bowel disease.

Bioinformatics (Oxford, England)
SUMMARY: Gene-based supervised machine learning classification models have been widely used to differentiate disease states, predict disease progression and determine effective treatment options. However, many of these classifiers are sensitive to no...

A review on machine learning principles for multi-view biological data integration.

Briefings in bioinformatics
Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological syste...

Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.

IEEE transactions on medical imaging
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build th...

Spatial Analysis of Functional Enrichment (SAFE) in Large Biological Networks.

Methods in molecular biology (Clifton, N.J.)
Spatial analysis of functional enrichment (SAFE) is a systematic quantitative approach for annotating large biological networks. SAFE detects network regions that are statistically overrepresented for functional groups or quantitative phenotypes of i...