AI Medical Compendium Journal:
BMC genomics

Showing 111 to 120 of 132 articles

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

BMC genomics
BACKGROUND: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by di...

SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare?

BMC genomics
BACKGROUND: A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to...

An empirical fuzzy multifactor dimensionality reduction method for detecting gene-gene interactions.

BMC genomics
BACKGROUND: Detection of gene-gene interaction (GGI) is a key challenge towards solving the problem of missing heritability in genetics. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. MDR reduces the...

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

BMC genomics
BACKGROUND: Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying patterns of pathway perturbations would provide insights into common disease mechanis...

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

BMC genomics
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging be...

Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

BMC genomics
BACKGROUND: The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinfor...

Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles.

BMC genomics
BACKGROUND: Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarker...

Stable feature selection based on the ensemble L -norm support vector machine for biomarker discovery.

BMC genomics
BACKGROUND: Lately, biomarker discovery has become one of the most significant research issues in the biomedical field. Owing to the presence of high-throughput technologies, genomic data, such as microarray data and RNA-seq, have become widely avail...

An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA.

BMC genomics
BACKGROUND: The ability to engineer zinc finger proteins binding to a DNA sequence of choice is essential for targeted genome editing to be possible. Experimental techniques and molecular docking have been successful in predicting protein-DNA interac...

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

BMC genomics
BACKGROUND: The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a ...