AI Medical Compendium Journal:
BMC genomics

Showing 71 to 80 of 132 articles

iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

BMC genomics
BACKGROUND: Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, ...

Classification of adaptor proteins using recurrent neural networks and PSSM profiles.

BMC genomics
BACKGROUND: Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-sign...

JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants.

BMC genomics
BACKGROUND: Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energ...

An improved catalogue of putative synaptic genes defined exclusively by temporal transcription profiles through an ensemble machine learning approach.

BMC genomics
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every p...

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.

BMC genomics
BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the ...

Knowledge Base Commons (KBCommons) v1.1: a universal framework for multi-omics data integration and biological discoveries.

BMC genomics
BACKGROUND: Knowledge Base Commons (KBCommons) v1.1 is a universal and all-inclusive web-based framework providing generic functionalities for storing, sharing, analyzing, exploring, integrating and visualizing multiple organisms' genomics and integr...

A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine.

BMC genomics
BACKGROUND: Recent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating ...

Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

BMC genomics
BACKGROUND: Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE).

Deciphering epigenomic code for cell differentiation using deep learning.

BMC genomics
BACKGROUND: Although DNA sequence plays a crucial role in establishing the unique epigenome of a cell type, little is known about the sequence determinants that lead to the unique epigenomes of different cell types produced during cell differentiatio...

HetEnc: a deep learning predictive model for multi-type biological dataset.

BMC genomics
BACKGROUND: Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a singl...