We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes. Our framework stands out for its ability to provide a deeper ...

Aging is a complex biological process involving multiple genes that are also related to phenotypic abnormalities. However, the micro view of the associations between aging and human phenotypic abnormalities is still under-studied. We propose a novel ...

Psoriasis is one of the inflammatory skin diseases where skin cells divide at a faster rate. The present study aims to perform mRNA analysis of geo datasets GSE166388. The differentially expressed genes (DEGs) were selected and gene ontology (GO) and ...

Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes with the same function. Existing research efforts of GO mainly focus on ...

Gene expression data from next-generation sequencing experiments enable advanced analysis of patterns related to gene function. Due to the large number of genes in experimental design (e.g. 20,000 genes), typical analyses focus solely on differential ...

Obesity increases the risk of diabetes; however, not everyone who is obese develops diabetes. In this study, the dataset GSE54350 was downloaded from the Gene Expression Omnibus database (GEO), including obese diabetic and non-diabetic samples as ...

Genes are known to play a pivotal role in Breast cancer. Any factor that that leads to a change in the expression level of the genes influences the mechanism and disturbs the functionality. Hence, our study aims to identify the key genes in the MCF-7 ...

The use of an ontology is a prevailing trend for management and analysis of biological big data. Consequently, we have encountered strong demands on developing algorithms for accurate analysis of ontology structures and annotated data. We can discover the ...

DNA microarray technology has led to huge advancement in the diagnosis process of diseases. In this paper, we have developed a framework to analyze gene expression data of three different Hepatitis C related prognosis datasets. First, we have discovered ...

Ontologies have proven to be useful for capturing and organizing knowledge as a hierarchical set of terms and their relationships. However, curating gene ontology data by hand requires specialized knowledge of certain field, which is inefficient. Thus ...

Microarray is a common technique used to identify differentially expressed genes for a target concept. The output is demonstrated in a heatmap, and biologists analyze related terms of gene ontology to identify characteristics of differentially expressed ...

Membrane proteins play important roles in various biological processes within organisms. Predicting the functional types of membrane proteins is indispensable to the characterization of membrane proteins. Recent studies have extended to predicting single-...

Modeling functions is a key aspect of artifact design, including software engineering and business systems modeling, but it is likewise of fundamental importance in natural systems modeling, for example in modeling biological organisms. The Unified ...

Gene expression data analysis is used in several areas including drug discovery and clinical applications. Biclustering in gene expression data is a subset of the genes representing consistent patterns over a subset of the conditions. In this case the ...

Gene Ontology(GO) is one of the most popular bioinformatic resource to study functional relationships between genes. However, the existing tools for calculating the semantic similarity are limited since they only rely on GO annotations and structure. We ...

Identifying condition-specific co-expressed gene groups is critical for gene functional and regulatory analysis. However, given that genes with critical functions (such as transcription factors) may not co-express with their target genes, it is ...

In the context of the classification task of data mining or machine learning, hierarchical feature selection methods exploit hierarchical relationships among features in order to select a subset of features without hierarchical redundancy. Hierarchical ...

A set of candidate genes for schizophrenia are selected and the chromosomal region around them (+/-3mb) is explored. The genes/ORF's in the vicinity of the candidate genes are searched for their properties using Gene Ontology. The data is processed and ...

In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to ...

Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, ...