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BackgroundPilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours.MethodsRNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization.ResultsIn this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions.ConclusionsThese results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer.

In July 2017, a 68-year-old woman in Pennsylvania, USA, sought care for fever, fatigue, cough, and abdominal pain. Her medical history was significant for common variable immunodeficiency and splenic B [...]

Abstract Background The technological advances in the past decade have lead to massive progress in the field of biotechnology. The documentation of the progress made exists in the form of research articles. The PubMed is the current most used repository for bio-literature. PubMed consists of about 17 million abstracts as of 2007 that require methods to efficiently retrieve and browse large volume of relevant information. The State-of-the-art technologies such as GOPubmed use simple keyword-based techniques for retrieving abstracts from the PubMed and linking them to the Gene Ontology (GO). This paper changes the paradigm by introducing semantics enabled technique to link the PubMed to the Gene Ontology, called, SEGOPubmed for ontology-based browsing. Latent Semantic Analysis (LSA) framework is used to semantically interface PubMed abstracts to the Gene Ontology. Results The Empirical analysis is performed to compare the performance of the SEGOPubmed with the GOPubmed. The analysis is initially performed using a few well-referenced query words. Further, statistical analysis is performed using GO curated dataset as ground truth. The analysis suggests that the SEGOPubmed performs better than the classic GOPubmed as it incorporates semantics. Conclusions The LSA technique is applied on the PubMed abstracts obtained based on the user query and the semantic similarity between the query and the abstracts. The analyses using well-referenced keywords show that the proposed semantic-sensitive technique outperformed the string comparison based techniques in associating the relevant abstracts to the GO terms. The SEGOPubmed also extracted the abstracts in which the keywords do not appear in isolation (i.e. they appear in combination with other terms) that could not be retrieved by simple term matching techniques.

In a recent work, we demonstrated the usefulness of the Hilbert transform in identifying the in-plane rotation angle between two objects. Here we use the Hilbert-wavelet bases instead of the Hilbert transform in the determination of the exact angle of rotation. We describe the design of the two-dimensional Hilbert-wavelet filter based on the spectral-factorization method to generate a Hilbert-transform pair of orthogonal wavelet bases. We compare the relative performance of the Hilbert transform and the Hilbert wavelet to identify both in-plane and out-of-plane rotation angles. We demonstrate that the Hilbert wavelet offers better rotation-angle determination than the Hilbert transform. We present correlation based rotated and scaled object identification and tracking using Hilbert or Hilbert-wavelet transformed infrared image sequences. We also demonstrate reduced data handling and improved tracking of distorted objects using the Hilbert-wavelet transform. OCIS codes: 100.5010, 100.7410, 100.2960.

Abstract Background Pilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours. Methods RNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization. Results In this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions. Conclusions These results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer.

Abstract Background This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. Results The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. Conclusion This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

A patient in Pennsylvania, USA, with common variable immunodeficiency sought care for fever, cough, and abdominal pain. Imaging revealed lesions involving multiple organs. Liver resection demonstrated necrotizing granulomas, recognizable tegument, and calcareous corpuscles indicative of an invasive cestode infection. Sequencing revealed 98% identity to a Versteria species of cestode found in mink. [ABSTRACT FROM AUTHOR]