AI Medical Compendium Topic

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Saccharomyces cerevisiae

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Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

Bioinformatics (Oxford, England)
MOTIVATION: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena ...

LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets o...

Extreme learning machines for reverse engineering of gene regulatory networks from expression time series.

Bioinformatics (Oxford, England)
MOTIVATION: The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regu...

A postprocessing method in the HMC framework for predicting gene function based on biological instrumental data.

The Review of scientific instruments
Predicting gene function based on biological instrumental data is a complicated and challenging hierarchical multi-label classification (HMC) problem. When using local approach methods to solve this problem, a preliminary results processing method is...

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...

DextMP: deep dive into text for predicting moonlighting proteins.

Bioinformatics (Oxford, England)
MOTIVATION: Moonlighting proteins (MPs) are an important class of proteins that perform more than one independent cellular function. MPs are gaining more attention in recent years as they are found to play important roles in various systems including...

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierar...

Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

Combinatorial chemistry & high throughput screening
BACKGROUND: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big pro...

Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning.

Lab on a chip
Monitoring yeast cell viability and concentration is important in brewing, baking and biofuel production. However, existing methods of measuring viability and concentration are relatively bulky, tedious and expensive. Here we demonstrate a compact an...