AIMC Topic: Computational Biology

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DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.

Bioinformatics (Oxford, England)
SUMMARY: Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computati...

Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.

Computers in biology and medicine
BACKGROUND: Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists...

PFP/ESG: automated protein function prediction servers enhanced with Gene Ontology visualization tool.

Bioinformatics (Oxford, England)
UNLABELLED: Protein function prediction (PFP) is an automated function prediction method that predicts Gene Ontology (GO) annotations for a protein sequence using distantly related sequences and contextual associations of GO terms. Extended similarit...

A multi-technique approach to bridge electronic case report form design and data standard adoption.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, i...

A natural language processing pipeline for pairing measurements uniquely across free-text CT reports.

Journal of biomedical informatics
OBJECTIVE: To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We st...

Kernel methods for large-scale genomic data analysis.

Briefings in bioinformatics
Machine learning, particularly kernel methods, has been demonstrated as a promising new tool to tackle the challenges imposed by today's explosive data growth in genomics. They provide a practical and principled approach to learning how a large numbe...

Alpha-plane based automatic general type-2 fuzzy clustering based on simulated annealing meta-heuristic algorithm for analyzing gene expression data.

Computers in biology and medicine
This paper considers microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of α-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering app...

Identification of mitochondria metabolism-related biomarkers associated with the development of rheumatoid arthritis using bioinformatics: An observational study.

Medicine
Rheumatoid arthritis (RA) is a systemic inflammatory autoimmune disorder that has serious physical and mental health implications. It is evident that disruptions to mitochondrial function have a considerable impact on the survival, activation, and di...

Unraveling risk factors and transcriptomic signatures in liver cancer progression and mortality through machine learning and bioinformatics.

Briefings in functional genomics
Liver cancer (LC) is the second leading cause of cancer-related deaths globally, yet the molecular mechanisms linking its progression with associated risk factors (RFs) remain poorly understood. To address this, we developed an integrative multi-stag...

Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.

Briefings in functional genomics
Genomic imprinting is an epigenetic occurrence that results in the expression of alleles specific to the parent of origin, plays pivotal roles in plant development, stress adaptation, and agronomic trait regulation. While imprinting has been intensiv...