Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stag... more Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stages of cancer by using conventional methods. The gene expression microarray technology is applied to detect and diagnose most types of cancers in their early stages. Furthermore, it allows researchers to analyze thousands of genes simultaneously. To acquire knowledge from gene expression data, data mining methods are needed. Due to the rapid evolution of cancer detection and diagnosis techniques, a survey of modern techniques is desirable. This study reviews and provide a detailed description of these techniques. As a result, it helps to enhance existing techniques for cancer detection and diagnosis as well as guiding researchers to develop new approaches.
2017 9th IEEE-GCC Conference and Exhibition (GCCCE), 2017
Wavelet transforms are part of general concept of multires-olutional theory that matured in image... more Wavelet transforms are part of general concept of multires-olutional theory that matured in image compression and gained high popularity in other image processing fields. One of the successful use was in medical engineering, in particular, computer-aided techniques for early breast cancer detection and diagnosis. Different wavelets transforms are employed in different phases to enhance the process of detection and diagnosis. This work reviews literature related with wavelets transforms in the early breast cancer detection and diagnosis.
Documenting the interaction of HTLV virus proteins with those of the host cell is crucial to unde... more Documenting the interaction of HTLV virus proteins with those of the host cell is crucial to understanding the process of the virus replication and pathogenesis, and provides an essential foundation for the development of safe and effective therapeutic treatments. Although numerous interactions have been reported in the scientific literature and various databases there is currently no method for efficiently accessing this information. In this paper we report on a project to design and implement mechanisms to extract and harvest this information on a continuous basis to compile a comprehensive, up-todate digital library of the described interactions between HTLV and cellular proteins. We have added a visualization service to the digital library that allows researchers to view the interaction network and manipulate it to narrow the choices of future experiments that will validate hypotheses about the various biological processes.
Graph Databases have been used widely in different areas. Owing to the type of representation the... more Graph Databases have been used widely in different areas. Owing to the type of representation they offer, they have gained popularity in disciplines where the interconnection of the data is a substantial matter. With the amount of interconnected data that the era of omics has resulted in, analyzing this data is an important task in medicine, drug design, and many other related fields. This can be done with the help of graph databases. In this paper, a novel multi-bipartite heterogeneous biological graph model is provided. It has been implemented and stored in the graph database Neo4j. Moreover, a new modified version of degree centrality (hereafter ”Disease Degree Centrality”) is adapted to aid in extracting and mining for meaningful insights from the graph model in hand. We calculated the Disease Degree Centrality for the intended node and we reported the most important protein domains. Finally, we analysed our results on a case study of Menkes and Wilson diseases using DAVID and I...
Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it ... more Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties...
Journal of Petroleum Science and Engineering, 2016
Abstract The static Young's modulus is one of the most important geomechanical parameters tha... more Abstract The static Young's modulus is one of the most important geomechanical parameters that are used in the evaluation of the wellbore stability during drilling operations. The static Young's modulus is also important during the design of the hydraulic and acid fracturing operations for conventional and unconventional reservoirs. Static Young's modulus also is important in the evaluation of the in-situ stresses profiles and it can be used to evaluate the reservoir pressures. The existing correlations that determine the static Young's modulus either depend on the dynamic Young's modulus or compressional shear velocity. No previous studies considered the different log parameters to estimate the static Young's modulus. Some of the previous correlations were developed for specific type of lithology and did not consider the lithology variation within the single well. In this paper and for the first time we developed correlations for the static Young's modulus from the log data based on clustering technique. More than 300 measured static Young's modulus values were correlated to the log parameters such as shear transit time, compressional transit time, and bulk density. Using R-project statistical software the clustering was performed for the measured data along with the corresponding log data. Six clusters were identified based on the shear transit time because it has the highest relative importance to the measured static Young's modulus. Six correlations were developed for the six clusters that can be used to determine the static Young's modulus based on the log data. The developed correlations were tested on three cases from the field given the measured core data for each case. The developed correlations based on clustering predicted the static Young's modulus perfectly when compared to the measured one for three different cases with different lithology sets. Other correlations did not match the measured static Young's modulus and bad match was obtained in several cases . Log parameters such as shear transit time, compressional transit time, and bulk density showed high relative importance to be included in the correlations that predict the static Young's modulus. Data clustering is a good method to apply to obtain better match between the estimated and measured static Young's modulus.
ABSTRACT It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary brea... more ABSTRACT It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary breast cancer. However, it is a tedious and expensive task to identify the mutant genes that impact breast cancer due to the large number of genes and very small number of samples. Furthermore, the expressive energy of the subset of genes in comparison to that of one individual gene at a time is considered to have a profound influence in case of breast cancer. In this paper 7 tumors with BRCA1 mutation and 8 tumors with BRCA2 mutation have been used to identify the subset of discriminative genes. A combination of a non-parametric supervised and an unsupervised statistical method is introduced to analyze the gene expressions and the distinctive genes among the highly expressed genes are identified. The most important genes are filtered using the area under the curve (AUC) measure. These filtered genes are then used to build a hidden Markov model (HMM) to analyse their inter-relationship and identify the best subset among them. In addition, Protein-Protein interaction network is generated to analyse the pathways of the identified genes and their link with BRCA1 or BRCA2. Transcription Factors are identified and Gene Set Enrichment Analysis (GSEA) is calculated for the identified genes subset and the results are compared with the results mentioned in other cancer literature. Experimental results suggest that only 8 genes have been identified out of 3226 genes by the proposed hybrid method. Out of the 8 identified genes, 5 have been linked with breast cancer by other studies. Moreover, 7 genes have been associated with numerous diseases that may result in breast cancer. Furthermore, 8 transcription factors were identified that cover the identified genes and BRCA1 and BRCA2. Lastly, GSEA enrichment score of 0.52 is calculated for the identified genes and it is comparatively better considering the small subset of identified genes.
Plasma and gas particle dynamics in atmospheric pressure helium-filled small volume are investiga... more Plasma and gas particle dynamics in atmospheric pressure helium-filled small volume are investigated using a two-dimensional model. The model includes the conservation equations for the plasma and the neutral gas. In this paper, results are presented from simulation of the interaction between gas and charged species, which in turn causes heating and thrust generation for this microengine. Gas heating and neutral depletion initiations are observed, highlighting the close interaction between gas and charged species in governing the evolution of the small-space plasma inside a microthruster designed for microsatellites.
Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in af... more Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in affecting the regional radiation budget. The long-term variability of these aerosols’ physical and optical parameters, including aerosol optical depth (AOD) and Ångström exponent (α), were measured at a location near central KSA using the Solar Village (SV) AERONET (Aerosol Robotic Network) station during the period December 1999–January 2013. The AERONET measurements show an overall increase in AOD on an annual basis. This upward trend is mainly attributed to a prolonged increase in the monthly/seasonal mean AOD during March–June and during August–September. In contrast, lower AOD values were observed during November–December. This can be attributed to a low frequency of dust outbreaks and higher precipitation rates. An overall, weak declining trend in α was observed, except during the summer. The spring and summer seasons experienced a pronounced increase in the number of coarse particle...
Protein-protein interaction networks are receiving increased attention due to their importance in... more Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are si...
... However, in the Igf-hi/better prognosis group, it is represented by a combination with AP2 (V... more ... However, in the Igf-hi/better prognosis group, it is represented by a combination with AP2 (VAP2GAMMA 01.wtmx.VHNF3Q601.wtmx) where as in the Igf-lo/poor prog-nosis group it is represented in combination with Egr1 (VEGR Q6.wtmx.VHNF3Q601.wtmx). ...
Understanding the interaction and crosstalk between pathways is important for understanding the f... more Understanding the interaction and crosstalk between pathways is important for understanding the function of both physiological and pathological biological systems. We have taken a computational approach to explore interactions among modules within biological networks by comparing and contrasting various topological measures which may be useful in the identification and prediction of critical connectivity points between modules. Node degree, betweenness, bridges, and articulation points may define connections among modules with distinct functions. Structural holes are another topological feature of networks which are important in identifying the role of nodes in the relationships among subclusters of graphs. Structural holes separate non-redundant sources of information, sources that are more additive than overlapping. We explore the performance of these among protein-protein interactions in yeast, then apply them to gene networks derived from a cohort of early stage breast cancer patients in whom different levels of IGF ligand have been associated with differing outcomes. We compare the different approaches to identifying and ranking genes based on these measures to reveal clues about cross-talk and feedback mechanisms and their role in mediating communication and coordination among modules.
We describe a novel algorithm for identifying the modular structure of a protein interaction netw... more We describe a novel algorithm for identifying the modular structure of a protein interaction network by computing overlapping clusters. The network is initially decomposed into a high degree network and a residual subnetwork, and clusters are computed separately in both networks, before highly interconnected clusters in both networks are merged. We propose the concept of bridge proteins, proteins that are
We describe an approach to clustering the yeast protein-protein interaction network in order to i... more We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.
Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and ho... more Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.
Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stag... more Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stages of cancer by using conventional methods. The gene expression microarray technology is applied to detect and diagnose most types of cancers in their early stages. Furthermore, it allows researchers to analyze thousands of genes simultaneously. To acquire knowledge from gene expression data, data mining methods are needed. Due to the rapid evolution of cancer detection and diagnosis techniques, a survey of modern techniques is desirable. This study reviews and provide a detailed description of these techniques. As a result, it helps to enhance existing techniques for cancer detection and diagnosis as well as guiding researchers to develop new approaches.
2017 9th IEEE-GCC Conference and Exhibition (GCCCE), 2017
Wavelet transforms are part of general concept of multires-olutional theory that matured in image... more Wavelet transforms are part of general concept of multires-olutional theory that matured in image compression and gained high popularity in other image processing fields. One of the successful use was in medical engineering, in particular, computer-aided techniques for early breast cancer detection and diagnosis. Different wavelets transforms are employed in different phases to enhance the process of detection and diagnosis. This work reviews literature related with wavelets transforms in the early breast cancer detection and diagnosis.
Documenting the interaction of HTLV virus proteins with those of the host cell is crucial to unde... more Documenting the interaction of HTLV virus proteins with those of the host cell is crucial to understanding the process of the virus replication and pathogenesis, and provides an essential foundation for the development of safe and effective therapeutic treatments. Although numerous interactions have been reported in the scientific literature and various databases there is currently no method for efficiently accessing this information. In this paper we report on a project to design and implement mechanisms to extract and harvest this information on a continuous basis to compile a comprehensive, up-todate digital library of the described interactions between HTLV and cellular proteins. We have added a visualization service to the digital library that allows researchers to view the interaction network and manipulate it to narrow the choices of future experiments that will validate hypotheses about the various biological processes.
Graph Databases have been used widely in different areas. Owing to the type of representation the... more Graph Databases have been used widely in different areas. Owing to the type of representation they offer, they have gained popularity in disciplines where the interconnection of the data is a substantial matter. With the amount of interconnected data that the era of omics has resulted in, analyzing this data is an important task in medicine, drug design, and many other related fields. This can be done with the help of graph databases. In this paper, a novel multi-bipartite heterogeneous biological graph model is provided. It has been implemented and stored in the graph database Neo4j. Moreover, a new modified version of degree centrality (hereafter ”Disease Degree Centrality”) is adapted to aid in extracting and mining for meaningful insights from the graph model in hand. We calculated the Disease Degree Centrality for the intended node and we reported the most important protein domains. Finally, we analysed our results on a case study of Menkes and Wilson diseases using DAVID and I...
Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it ... more Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties...
Journal of Petroleum Science and Engineering, 2016
Abstract The static Young's modulus is one of the most important geomechanical parameters tha... more Abstract The static Young's modulus is one of the most important geomechanical parameters that are used in the evaluation of the wellbore stability during drilling operations. The static Young's modulus is also important during the design of the hydraulic and acid fracturing operations for conventional and unconventional reservoirs. Static Young's modulus also is important in the evaluation of the in-situ stresses profiles and it can be used to evaluate the reservoir pressures. The existing correlations that determine the static Young's modulus either depend on the dynamic Young's modulus or compressional shear velocity. No previous studies considered the different log parameters to estimate the static Young's modulus. Some of the previous correlations were developed for specific type of lithology and did not consider the lithology variation within the single well. In this paper and for the first time we developed correlations for the static Young's modulus from the log data based on clustering technique. More than 300 measured static Young's modulus values were correlated to the log parameters such as shear transit time, compressional transit time, and bulk density. Using R-project statistical software the clustering was performed for the measured data along with the corresponding log data. Six clusters were identified based on the shear transit time because it has the highest relative importance to the measured static Young's modulus. Six correlations were developed for the six clusters that can be used to determine the static Young's modulus based on the log data. The developed correlations were tested on three cases from the field given the measured core data for each case. The developed correlations based on clustering predicted the static Young's modulus perfectly when compared to the measured one for three different cases with different lithology sets. Other correlations did not match the measured static Young's modulus and bad match was obtained in several cases . Log parameters such as shear transit time, compressional transit time, and bulk density showed high relative importance to be included in the correlations that predict the static Young's modulus. Data clustering is a good method to apply to obtain better match between the estimated and measured static Young's modulus.
ABSTRACT It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary brea... more ABSTRACT It is well known that the mutations in BRCA1 or BRCA2 gene can cause the hereditary breast cancer. However, it is a tedious and expensive task to identify the mutant genes that impact breast cancer due to the large number of genes and very small number of samples. Furthermore, the expressive energy of the subset of genes in comparison to that of one individual gene at a time is considered to have a profound influence in case of breast cancer. In this paper 7 tumors with BRCA1 mutation and 8 tumors with BRCA2 mutation have been used to identify the subset of discriminative genes. A combination of a non-parametric supervised and an unsupervised statistical method is introduced to analyze the gene expressions and the distinctive genes among the highly expressed genes are identified. The most important genes are filtered using the area under the curve (AUC) measure. These filtered genes are then used to build a hidden Markov model (HMM) to analyse their inter-relationship and identify the best subset among them. In addition, Protein-Protein interaction network is generated to analyse the pathways of the identified genes and their link with BRCA1 or BRCA2. Transcription Factors are identified and Gene Set Enrichment Analysis (GSEA) is calculated for the identified genes subset and the results are compared with the results mentioned in other cancer literature. Experimental results suggest that only 8 genes have been identified out of 3226 genes by the proposed hybrid method. Out of the 8 identified genes, 5 have been linked with breast cancer by other studies. Moreover, 7 genes have been associated with numerous diseases that may result in breast cancer. Furthermore, 8 transcription factors were identified that cover the identified genes and BRCA1 and BRCA2. Lastly, GSEA enrichment score of 0.52 is calculated for the identified genes and it is comparatively better considering the small subset of identified genes.
Plasma and gas particle dynamics in atmospheric pressure helium-filled small volume are investiga... more Plasma and gas particle dynamics in atmospheric pressure helium-filled small volume are investigated using a two-dimensional model. The model includes the conservation equations for the plasma and the neutral gas. In this paper, results are presented from simulation of the interaction between gas and charged species, which in turn causes heating and thrust generation for this microengine. Gas heating and neutral depletion initiations are observed, highlighting the close interaction between gas and charged species in governing the evolution of the small-space plasma inside a microthruster designed for microsatellites.
Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in af... more Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in affecting the regional radiation budget. The long-term variability of these aerosols’ physical and optical parameters, including aerosol optical depth (AOD) and Ångström exponent (α), were measured at a location near central KSA using the Solar Village (SV) AERONET (Aerosol Robotic Network) station during the period December 1999–January 2013. The AERONET measurements show an overall increase in AOD on an annual basis. This upward trend is mainly attributed to a prolonged increase in the monthly/seasonal mean AOD during March–June and during August–September. In contrast, lower AOD values were observed during November–December. This can be attributed to a low frequency of dust outbreaks and higher precipitation rates. An overall, weak declining trend in α was observed, except during the summer. The spring and summer seasons experienced a pronounced increase in the number of coarse particle...
Protein-protein interaction networks are receiving increased attention due to their importance in... more Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are si...
... However, in the Igf-hi/better prognosis group, it is represented by a combination with AP2 (V... more ... However, in the Igf-hi/better prognosis group, it is represented by a combination with AP2 (VAP2GAMMA 01.wtmx.VHNF3Q601.wtmx) where as in the Igf-lo/poor prog-nosis group it is represented in combination with Egr1 (VEGR Q6.wtmx.VHNF3Q601.wtmx). ...
Understanding the interaction and crosstalk between pathways is important for understanding the f... more Understanding the interaction and crosstalk between pathways is important for understanding the function of both physiological and pathological biological systems. We have taken a computational approach to explore interactions among modules within biological networks by comparing and contrasting various topological measures which may be useful in the identification and prediction of critical connectivity points between modules. Node degree, betweenness, bridges, and articulation points may define connections among modules with distinct functions. Structural holes are another topological feature of networks which are important in identifying the role of nodes in the relationships among subclusters of graphs. Structural holes separate non-redundant sources of information, sources that are more additive than overlapping. We explore the performance of these among protein-protein interactions in yeast, then apply them to gene networks derived from a cohort of early stage breast cancer patients in whom different levels of IGF ligand have been associated with differing outcomes. We compare the different approaches to identifying and ranking genes based on these measures to reveal clues about cross-talk and feedback mechanisms and their role in mediating communication and coordination among modules.
We describe a novel algorithm for identifying the modular structure of a protein interaction netw... more We describe a novel algorithm for identifying the modular structure of a protein interaction network by computing overlapping clusters. The network is initially decomposed into a high degree network and a residual subnetwork, and clusters are computed separately in both networks, before highly interconnected clusters in both networks are merged. We propose the concept of bridge proteins, proteins that are
We describe an approach to clustering the yeast protein-protein interaction network in order to i... more We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.
Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and ho... more Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.
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Papers by emad ramadan