Curvature analysis of perisylvian epilepsy.

Purpose: We assess whether alterations in the convolutional anatomy of the deep perisylvian area (DPSA) might indicate focal epileptogenicity.

Materials And Methods: The DPSA of each hemisphere was segmented on MRI and a 3D gray-white matter interface (GWMI) geometrical model was constructed. Comparative visual and quantitative assessment of the convolutional anatomy of both the left and right DPSA models was performed.

Algorithmic lifestyle optimization.

Objective: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual.

Quantitative PCR for detection and quantification of Veronaea botryosa in fish and environmental samples.

Systemic phaeohyphomycosis, aka 'fluid belly', is one of the most important emergent diseases in sturgeon Acipenser spp. aquaculture. The etiologic agent is the saprobic, dematiaceous fungus Veronaea botryosa.

Methane and fatty acid metabolism pathways are predictive of Low-FODMAP diet efficacy for patients with irritable bowel syndrome.

Objective: Identification of microbiota-based biomarkers as predictors of low-FODMAP diet response and design of a diet recommendation strategy for IBS patients.

Design: We created a compendium of gut microbiome and disease severity data before and after a low-FODMAP diet treatment from published studies followed by unified data processing, statistical analysis and predictive modeling. We employed data-driven methods that solely rely on the compendium data, as well as hypothesis-driven methods that focus on methane and short chain fatty acid (SCFA) metabolism pathways that were implicated in the disease etiology.

Accelerated knowledge discovery from omics data by optimal experimental design.

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data.

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods.

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Motivation: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predictive models of gene expression with far reaching applications.

Results: We present the Genetic Neural Network (GNN), an artificial neural network for predicting genome-wide gene expression given gene knockouts and master regulator perturbations.

DeepPep: Deep proteome inference from peptide profiles.

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile.