RESUMEN

Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene-gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.

Asunto(s)

Biomarcadores de Tumor , Neoplasias Esofágicas , Carcinoma de Células Escamosas de Esófago , Regulación Neoplásica de la Expresión Génica , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Humanos , Carcinoma de Células Escamosas de Esófago/genética , Carcinoma de Células Escamosas de Esófago/patología , Análisis de la Célula Individual/métodos , Biomarcadores de Tumor/genética , Neoplasias Esofágicas/genética , Neoplasias Esofágicas/patología , Análisis de Secuencia de ARN/métodos , Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes/genética , RNA-Seq/métodos , Análisis de Expresión Génica de una Sola Célula

RESUMEN

One of the integral part of the network analysis is finding groups of nodes that exhibit similar properties. Community detection techniques are a popular choice to find such groups or communities within a network and it relies on graph-based methods to achieve this goal. Finding communities in biological networks such as gene co-expression networks are particularly important to find groups of genes where we can focus on further downstream analysis and find valuable insights regarding concerned diseases. Here, we present an effective community detection method called community detection using centrality-based approach (CDCA), designed using the graph centrality approach. The method has been tested using four benchmark bulk RNA-seq datasets for schizophrenia and bipolar disorder, and the performance has been proved superior in comparison to several other counterparts. The quality of communities are determined using intrinsic graph properties such as modularity and homogeneity. The biological significance of resultant communities is decided using the pathway enrichment analysis.

Asunto(s)

Redes Reguladoras de Genes , RNA-Seq , Esquizofrenia , Humanos , Esquizofrenia/genética , RNA-Seq/métodos , Trastorno Bipolar/genética , Algoritmos , Biología Computacional/métodos , Análisis de Secuencia de ARN/métodos , Perfilación de la Expresión Génica/métodos

RESUMEN

The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.

Asunto(s)

Biomarcadores de Tumor , Neoplasias Esofágicas , Carcinoma de Células Escamosas de Esófago , Humanos , Carcinoma de Células Escamosas de Esófago/genética , Neoplasias Esofágicas/genética , Biomarcadores de Tumor/genética , Algoritmos , Análisis por Conglomerados

RESUMEN

This paper presents a consensus-based approach that incorporates three microarray and three RNA-Seq methods for unbiased and integrative identification of differentially expressed genes (DEGs) as potential biomarkers for critical disease(s). The proposed method performs satisfactorily on two microarray datasets (GSE20347 and GSE23400) and one RNA-Seq dataset (GSE130078) for esophageal squamous cell carcinoma (ESCC). Based on the input dataset, our framework employs specific DE methods to detect DEGs independently. A consensus based function that first considers DEGs common to all three methods for further downstream analysis has been introduced. The consensus function employs other parameters to overcome information loss. Differential co-expression (DCE) and preservation analysis of DEGs facilitates the study of behavioral changes in interactions among DEGs under normal and diseased circumstances. Considering hub genes in biologically relevant modules and most GO and pathway enriched DEGs as candidates for potential biomarkers of ESCC, we perform further validation through biological analysis as well as literature evidence. We have identified 25 DEGs that have strong biological relevance to their respective datasets and have previous literature establishing them as potential biomarkers for ESCC. We have further identified 8 additional DEGs as probable potential biomarkers for ESCC, but recommend further in-depth analysis.

RESUMEN

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

RESUMEN

BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we wanted to expand the state-of-the-art in disease biomarker prediction from RNA-seq data using DL. However, application of DL to RNA-seq data is challenging due to absence of appropriate labels and smaller sample size as compared to number of genes. Deep learning coupled with transfer learning can improve prediction performance on novel data by incorporating patterns learned from other related data. With the emergence of new disease datasets, biomarker prediction would be facilitated by having a generalized model that can transfer the knowledge of trained feature maps to the new dataset. To the best of our knowledge, there is no Convolutional Neural Network (CNN)-based model coupled with transfer learning to predict the significant upregulating (UR) and downregulating (DR) genes from both trained and untrained datasets. RESULTS: We implemented a CNN model, DEGnext, to predict UR and DR genes from gene expression data obtained from The Cancer Genome Atlas database. DEGnext uses biologically validated data along with logarithmic fold change values to classify differentially expressed genes (DEGs) as UR and DR genes. We applied transfer learning to our model to leverage the knowledge of trained feature maps to untrained cancer datasets. DEGnext's results were competitive (ROC scores between 88 and 99[Formula: see text]) with those of five traditional machine learning methods: Decision Tree, K-Nearest Neighbors, Random Forest, Support Vector Machine, and XGBoost. DEGnext was robust and effective in terms of transferring learned feature maps to facilitate classification of unseen datasets. Additionally, we validated that the predicted DEGs from DEGnext were mapped to significant Gene Ontology terms and pathways related to cancer. CONCLUSIONS: DEGnext can classify DEGs into UR and DR genes from RNA-seq cancer datasets with high performance. This type of analysis, using biologically relevant fine-tuning data, may aid in the exploration of potential biomarkers and can be adapted for other disease datasets.

Asunto(s)

Neoplasias , Redes Neurales de la Computación , Humanos , Aprendizaje Automático , RNA-Seq , Máquina de Vectores de Soporte

RESUMEN

To promote diligent analysis of the progression of a disease, it is important to identify interesting biomarkers for the disease. Biclustering has already been established as an effective technique to help identify such biomarkers of high biological significance. Although in the recent past, a good number of biclustering techniques have been introduced, most of them fail to perform consistently across multiple domains or datasets. To choose a single biclustering technique that can help the accomplishment of such a critical task for multiple diseases with high precision is extremely difficult. Hence, in this study, we considered several biclustering techniques and accepted those techniques and their results which are found significant from enrichment perspective for subsequent analysis. Based on biclustering results, we constructed biological networks and carried out a topological, pathway and causal analysis on the modules extracted from the networks. Our multiobjective study enabled us to identify several biomarkers for esophageal squamous cell carcinoma (ESCC) such as IFNGR1, CLIC1, CDK4, and COPS5, after applying a ranking scheme.

Asunto(s)

Complejo del Señalosoma COP9/genética , Canales de Cloruro/genética , Quinasa 4 Dependiente de la Ciclina/genética , Neoplasias Esofágicas/genética , Carcinoma de Células Escamosas de Esófago/genética , Péptidos y Proteínas de Señalización Intracelular/genética , Proteínas de Neoplasias/genética , Péptido Hidrolasas/genética , Receptores de Interferón/genética , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Complejo del Señalosoma COP9/metabolismo , Canales de Cloruro/metabolismo , Análisis por Conglomerados , Biología Computacional/métodos , Quinasa 4 Dependiente de la Ciclina/metabolismo , Conjuntos de Datos como Asunto , Neoplasias Esofágicas/diagnóstico , Neoplasias Esofágicas/metabolismo , Neoplasias Esofágicas/patología , Carcinoma de Células Escamosas de Esófago/diagnóstico , Carcinoma de Células Escamosas de Esófago/metabolismo , Carcinoma de Células Escamosas de Esófago/patología , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Ontología de Genes , Redes Reguladoras de Genes , Humanos , Péptidos y Proteínas de Señalización Intracelular/metabolismo , Anotación de Secuencia Molecular , Proteínas de Neoplasias/metabolismo , Análisis de Secuencia por Matrices de Oligonucleótidos , Péptido Hidrolasas/metabolismo , Receptores de Interferón/metabolismo , Receptor de Interferón gamma

RESUMEN

Genes act in groups known as gene modules, which accomplish different cellular functions in the body. The modular nature of gene networks was used in this study to detect functionally enriched modules in samples obtained from COPD patients. We analyzed modules extracted from COPD samples and identified crucial genes associated with the disease COVID-19. We also extracted modules from a COVID-19 dataset and analyzed a suspected set of genes that may be associated with this deadly disease. We used information available for two other viruses that cause SARS and MERS because their physiology is similar to that of the COVID-19 virus. We report several crucial genes associated with COVID-19: RPA2, POLD4, MAPK8, IRF7, JUN, NFKB1, NFKBIA, CD40LG, FASLG, ICAM1, LIFR, STAT2 and CCR1. Most of these genes are related to the immune system and respiratory organs, which emphasizes the fact that COPD weakens this system and makes patients more susceptible to developing severe COVID-19.

Asunto(s)

COVID-19/genética , Bases de Datos de Ácidos Nucleicos , Predisposición Genética a la Enfermedad , Enfermedad Pulmonar Obstructiva Crónica/genética , SARS-CoV-2/genética , COVID-19/inmunología , Humanos , Enfermedad Pulmonar Obstructiva Crónica/inmunología , Enfermedad Pulmonar Obstructiva Crónica/virología , SARS-CoV-2/inmunología , Índice de Severidad de la Enfermedad

RESUMEN

A gene co-expression network (CEN) is of biological interest, since co-expressed genes share common functions and biological processes or pathways. Finding relationships among modules can reveal inter-modular preservation, and similarity in transcriptome, functional, and biological behaviors among modules of the same or two different datasets. There is no method which explores the one-to-one relationships and one-to-many relationships among modules extracted from control and disease samples based on both topological and semantic similarity using both microarray and RNA seq data. In this work, we propose a novel fusion measure to detect mapping between modules from two sets of co-expressed modules extracted from control and disease stages of Alzheimer's disease (AD) and Parkinson's disease (PD) datasets. Our measure considers both topological and biological information of a module and is an estimation of four parameters, namely, semantic similarity, eigengene correlation, degree difference, and the number of common genes. We analyze the consensus modules shared between both control and disease stages in terms of their association with diseases. We also validate the close associations between human and chimpanzee modules and compare with the state-ofthe- art method. Additionally, we propose two novel observations on the relationships between modules for further analysis.

Asunto(s)

Regulación de la Expresión Génica , Redes Reguladoras de Genes/fisiología , Transcriptoma , Algoritmos , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Animales , Bases de Datos Genéticas , Humanos , Pan troglodytes , Enfermedad de Parkinson/genética , Enfermedad de Parkinson/metabolismo

RESUMEN

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.

Asunto(s)

Enfermedad de Alzheimer , Bases de Datos Genéticas , Perfilación de la Expresión Génica , Redes Reguladoras de Genes , Enfermedad de Parkinson , Transcriptoma , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Biomarcadores/metabolismo , Humanos , Enfermedad de Parkinson/genética , Enfermedad de Parkinson/metabolismo

RESUMEN

Developing a cost-effective and robust triclustering algorithm that can identify triclusters of high biological significance in the gene-sample-time (GST) domain is a challenging task. Most existing triclustering algorithms can detect shifting and scaling patterns in isolation, they are not able to handle co-occurring shifting-and-scaling patterns. This paper makes an attempt to address this issue. It introduces a robust triclustering algorithm called THD-Tricluster to identify triclusters over the GST domain. In addition to applying over several benchmark datasets for its validation, the proposed THD-Tricluster algorithm was applied on HIV-1 progression data to identify disease-specific genes. THD-Tricluster could identify 38 most responsible genes for the deadly disease which includes GATA3, EGR1, JUN, ELF1, AGFG1, AGFG2, CX3CR1, CXCL12, CCR5, CCR2, and many others. The results are validated using GeneCard and other established results.

Asunto(s)

Algoritmos , VIH-1/genética , Análisis por Conglomerados , VIH-1/aislamiento & purificación , Humanos , Análisis de Secuencia por Matrices de Oligonucleótidos

RESUMEN

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.

RESUMEN

Advancement in science has tended to improve treatment of fatal diseases such as cancer. A major concern in the area is the spread of cancerous cells, technically refered to as metastasis into other organs beyond the primary organ. Treatment in such a stage of cancer is extremely difficult and usually palliative only. In this study, we focus on finding gene-gene network modules which are functionally similar in nature in the case of breast cancer. These modules extracted during the disease progression stages are analyzed using p-value and their associated pathways. We also explore interesting patterns associated with the causal genes, viz., SCGB1D2, MET, CYP1B1 and MMP9 in terms of expression similarity and pathway contexts. We analyze the genes involved in both the stages- non metastasis and metastatsis and change in their expression values, their associated pathways and roles as the disease progresses from one stage to another. We discover three additional pathways viz., Glycerophospholipid metablism, h-Efp pathway and CARM1 and Regulation of Estrogen Receptor, which can be related to the metastasis phase of breast cancer. These new pathways can be further explored to identify their relevance during the progression of the disease.

Asunto(s)

Biomarcadores de Tumor/análisis , Neoplasias de la Mama/patología , Neoplasias de la Mama/secundario , Redes Reguladoras de Genes , Neoplasias de la Mama/diagnóstico , Progresión de la Enfermedad , Femenino , Glicerofosfolípidos/metabolismo , Humanos , Proteína-Arginina N-Metiltransferasas/análisis , Receptores de Estrógenos/análisis , Factores de Transcripción/análisis , Proteínas de Motivos Tripartitos/análisis , Ubiquitina-Proteína Ligasas/análisis

RESUMEN

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein-protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast and a few other model organisms. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rank diseased complexes based on a query disease. We have shown that it has better performance in identifying protein complexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy. We have introduced a ranking approach and showed its application on Alzheimer's disease.

Asunto(s)

Algoritmos , Enfermedad de Alzheimer/metabolismo , Biología Computacional/métodos , Mapeo de Interacción de Proteínas/estadística & datos numéricos , Enfermedad de Alzheimer/diagnóstico , Enfermedad de Alzheimer/patología , Bases de Datos de Proteínas , Humanos , Unión Proteica

RESUMEN

There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer's disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer's disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer's disease brains. The biological pathways associated with Alzheimer's disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.

Asunto(s)

Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/metabolismo , Minería de Datos/métodos , Bases de Datos de Ácidos Nucleicos , Regulación de la Expresión Génica , Femenino , Humanos , Masculino

RESUMEN

Mining microarray data to unearth interesting expression profile patterns for discovery of in silico biological knowledge is an emerging area of research in computational biology. A group of functionally related genes may have similar expression patterns under a set of conditions or at some time points. Biclustering is an important data mining tool that has been successfully used to analyze gene expression data for biologically significant cluster discovery. The purpose of this chapter is to introduce interesting patterns that may be observed in expression data and discuss the role of biclustering techniques in detecting interesting functional gene groups with similar expression patterns.

Asunto(s)

Análisis por Conglomerados , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Animales , Minería de Datos/métodos , Regulación de la Expresión Génica , Humanos , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Reproducibilidad de los Resultados

RESUMEN

A number of methods have been proposed in the literature of protein-protein interaction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well-known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results.

Asunto(s)

Mapas de Interacción de Proteínas , Proteínas/química , Análisis por Conglomerados , Bases de Datos de Proteínas , Unión Proteica , Mapeo de Interacción de Proteínas , Reproducibilidad de los Resultados

RESUMEN

A number of clustering methods introduced for analysis of gene expression data for extracting potential relationships among the genes are studied and reported in this paper. An effective unsupervised method (TDAC) is proposed for simultaneous detection of outliers and biologically relevant co-expressed patterns. Effectiveness of TDAC is established in comparison to its other competing algorithms over six publicly available benchmark gene expression datasets in terms of both internal and external validity measures. Main attractions of TDAC are: (a) it does not require discretisation, (b) it is capable of identifying biologically relevant gene co-expressed patterns as well as outlier genes(s), (c) it is cost-effective in terms of time and space, (d) it does not require the number of clusters a priori, and (e) it is free from the restrictions of using any proximity measure.

Asunto(s)

Algoritmos , Perfilación de la Expresión Génica/métodos , Modelos Biológicos , Mapeo de Interacción de Proteínas/métodos , Proteoma/metabolismo , Transducción de Señal/fisiología , Simulación por Computador

RESUMEN

BACKGROUND: Biological networks connect genes, gene products to one another. A network of co-regulated genes may form gene clusters that can encode proteins and take part in common biological processes. A gene co-expression network describes inter-relationships among genes. Existing techniques generally depend on proximity measures based on global similarity to draw the relationship between genes. It has been observed that expression profiles are sharing local similarity rather than global similarity. We propose an expression pattern based method called GeCON to extract Gene CO-expression Network from microarray data. Pair-wise supports are computed for each pair of genes based on changing tendencies and regulation patterns of the gene expression. Gene pairs showing negative or positive co-regulation under a given number of conditions are used to construct such gene co-expression network. We construct co-expression network with signed edges to reflect up- and down-regulation between pairs of genes. Most existing techniques do not emphasize computational efficiency. We exploit a fast correlogram matrix based technique for capturing the support of each gene pair to construct the network. RESULTS: We apply GeCON to both real and synthetic gene expression data. We compare our results using the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge data with three well known algorithms, viz., ARACNE, CLR and MRNET. Our method outperforms other algorithms based on in silico regulatory network reconstruction. Experimental results show that GeCON can extract functionally enriched network modules from real expression data. CONCLUSIONS: In view of the results over several in-silico and real expression datasets, the proposed GeCON shows satisfactory performance in predicting co-expression network in a computationally inexpensive way. We further establish that a simple expression pattern matching is helpful in finding biologically relevant gene network. In future, we aim to introduce an enhanced GeCON to identify Protein-Protein interaction network complexes by incorporating variable density concept.

Asunto(s)

Perfilación de la Expresión Génica , Redes Reguladoras de Genes , Análisis de Secuencia por Matrices de Oligonucleótidos , Algoritmos , Simulación por Computador , Regulación hacia Abajo , Expresión Génica , Perfilación de la Expresión Génica/métodos , Humanos , Modelos Genéticos , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos

RESUMEN

BACKGROUND: The development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules. RESULTS: This paper presents a method to build a co-expression network (CEN) and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity) to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value. CONCLUSIONS: Our results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.

Asunto(s)

Biología Computacional/métodos , Interpretación Estadística de Datos , Perfilación de la Expresión Génica/estadística & datos numéricos , Redes Reguladoras de Genes , Análisis de Secuencia por Matrices de Oligonucleótidos/estadística & datos numéricos , Algoritmos , Bases de Datos Genéticas/estadística & datos numéricos , Expresión Génica