AIMC Topic: Gene Expression Profiling
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A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.
BMC genomics
Dec 22, 2016
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 ...
Gogadget: An R Package for Interpretation and Visualization of GO Enrichment Results.
Molecular informatics
Dec 21, 2016
Gene expression profiling followed by gene ontology (GO) term enrichment analysis can generate long lists of significant GO terms. To interpret these results and get biological insight in the data, filtering and rearranging these long lists of GO ter...
Identification of DEP domain-containing proteins by a machine learning method and experimental analysis of their expression in human HCC tissues.
Scientific reports
Dec 21, 2016
The Dishevelled/EGL-10/Pleckstrin (DEP) domain-containing (DEPDC) proteins have seven members. However, whether this superfamily can be distinguished from other proteins based only on the amino acid sequences, remains unknown. Here, we describe a com...
Humans
Machine Learning
Algorithms
Phylogeny
Gene Expression Regulation, Neoplastic
Signal Transduction
Carcinoma, Hepatocellular
Cluster Analysis
GTPase-Activating Proteins
Protein Domains
Computational Biology
Gene Expression Profiling
Dishevelled Proteins
Amino Acid Motifs
RNA, Messenger
Real-Time Polymerase Chain Reaction
Liver Neoplasms
Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes.
BMC bioinformatics
Dec 19, 2016
BACKGROUND: Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain...
Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.
PloS one
Dec 9, 2016
This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection su...
Introducing a Stable Bootstrap Validation Framework for Reliable Genomic Signature Extraction.
IEEE/ACM transactions on computational biology and bioinformatics
Nov 29, 2016
The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signature...
Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.
BioMed research international
Nov 24, 2016
. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous chall...
Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants.
Scientific reports
Nov 11, 2016
Respiratory syncytial virus (RSV) causes infections that range from common cold to severe lower respiratory tract infection requiring high-level medical care. Prediction of the course of disease in individual patients remains challenging at the first...
Artificial neural network classifier predicts neuroblastoma patients' outcome.
BMC bioinformatics
Nov 8, 2016
BACKGROUND: More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly va...
A recurrence model for laryngeal cancer based on SVM and gene function clustering.
Acta oto-laryngologica
Nov 4, 2016
CONCLUSION: A prognostic model was obtained for LC. Several critical genes were unveiled. They could be potentially applied for LC recurrence prediction.
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