AIMC Topic: Computational Biology

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Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays...

ProClaT, a new bioinformatics tool for in silico protein reclassification: case study of DraB, a protein coded from the draTGB operon in Azospirillum brasilense.

BMC bioinformatics
BACKGROUND: Azopirillum brasilense is a plant-growth promoting nitrogen-fixing bacteria that is used as bio-fertilizer in agriculture. Since nitrogen fixation has a high-energy demand, the reduction of N to NH by nitrogenase occurs only under limitin...

Spectral consensus strategy for accurate reconstruction of large biological networks.

BMC bioinformatics
BACKGROUND: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with f...

On the inconsistency of ℓ -penalised sparse precision matrix estimation.

BMC bioinformatics
BACKGROUND: Various ℓ -penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consisten...

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.

Scientific reports
We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem c...

Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility.

Proteins
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...

Evolvix BEST Names for semantic reproducibility across code2brain interfaces.

Annals of the New York Academy of Sciences
Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward...

A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli.

Artificial intelligence in medicine
BACKGROUND: Identifying transcription factors binding sites (TFBSs) plays an important role in understanding gene regulatory processes. The underlying mechanism of the specific binding for transcription factors (TFs) is still poorly understood. Previ...

Expansion of the Gene Ontology knowledgebase and resources.

Nucleic acids research
The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our conti...

The Human Phenotype Ontology in 2017.

Nucleic acids research
Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.hum...