AI Medical Compendium Journal:
BMC genomics

Showing 101 to 110 of 132 articles

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

BMC genomics
BACKGROUND: Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and H...

Identification of recent cases of hepatitis C virus infection using physical-chemical properties of hypervariable region 1 and a radial basis function neural network classifier.

BMC genomics
BACKGROUND: Identification of acute or recent hepatitis C virus (HCV) infections is important for detecting outbreaks and devising timely public health interventions for interruption of transmission. Epidemiological investigations and chemistry-based...

A deep auto-encoder model for gene expression prediction.

BMC genomics
BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expressio...

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BMC genomics
BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed whe...

Phylogeny analysis from gene-order data with massive duplications.

BMC genomics
BACKGROUND: Gene order changes, under rearrangements, insertions, deletions and duplications, have been used as a new type of data source for phylogenetic reconstruction. Because these changes are rare compared to sequence mutations, they allow the i...

Semantic biclustering for finding local, interpretable and predictive expression patterns.

BMC genomics
BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of under...

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.

BMC genomics
BACKGROUND: Cell-penetrating peptides (CPPs) are short peptides (5-30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer trea...

DRREP: deep ridge regressed epitope predictor.

BMC genomics
INTRODUCTION: The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been nume...

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

BMC genomics
BACKGROUND: Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in th...

A comparison of machine learning and Bayesian modelling for molecular serotyping.

BMC genomics
BACKGROUND: Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays p...