Characterizing the origin of high-grade serous ovarian cancer has significant practical importance for advancing biological knowledge and improving clinical treatments. Rapid advances in molecular ...profiling technologies and machine learning based data analytics provide new opportunities to investigate this important question using data-driven approaches at the molecular and network levels. We now report novel analytic results in assessing the origin of high-grade serous ovarian carcinoma. Using genome-wide gene expression data and effective machine learning approaches, we design proper statistical significance tests and perform both genomic and network analyses to discriminate among three possible origins. The experimental results are consistent with recent scientific hypothesis and independent findings.
While the etiology and mechanisms underpinning the pathogenesis of autism spectrum disorder (ASD) continue to be under investigation, identifying unique metabolomic patterns in children with ASD is ...one promising avenue. Re-examining the data collected from a nutritional supplementation study on a cohort of children with ASD, this work seeks to better elucidate the mechanisms behind how nutritional intervention impacts changes in recipients’ behavior and underlying metabolomics. Among biochemical measurements that were identified at baseline as being significant distinct between typically developing and ASD cohorts, 50% no longer significantly differed after intervention. Furthermore, among the correlational relationships observed, nutritional intervention was associated with a shift towards correlations between metabolites more like that of a comparable typically developing cohort.
Since experiments involving animal models are labor and time intensive, there is an attempt to replace these measurements on animal models with in vitro assays which has higher acceptance in the ...population concerning ethical issues. In this work, we explore to what extend animal models can be replaced by in vitro assays in the context of a toxicogenomics study. The data from the Japanese Toxicogenomics Project are gene expression profiles measured by microarrays from both in vitro and animal samples. We apply a comprehensive genomic association network analysis in order to study the comparative behavior of the genomic networks for the in vivo vs. in vitro data. The genomic networks are computed based on association scores of gene-gene pairs using a partial least squares modeling of gene expression values adjusted for sacrifice time and dosage. We apply permutation based statistical tests to compare the connectivity of a given gene, as well as a class of genes in the two networks which may be affected by a given drug. The goal is to identify parts of these networks including key genes that are not significantly altered for in vivo vs. in vitro samples for the majority of the drugs.
Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, ...properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our findings also provide evidence for significant differences between networks of normal and tumor samples, even after accounting for the considerable variability in structures of networks estimated using different reconstruction methods. These differences can offer new insight into changes in mechanisms of genetic interaction associated with cancer initiation and progression.
Co-infection with tuberculosis (TB) is the preeminent cause of demise in human immunodeficiency virus (HIV) infected individuals. However, diagnosis of TB, particularly in the presence of an HIV ...co-infection, can be limiting owing to the high inaccuracy associated with conventional diagnostic strategies. Here we determine dysregulated pathways in TB-HIV co-infection and HIV infection utilizing coexpression networks. Primarily, we utilized preservation statistics to identify gene modules that exhibit a weak conservation of network topology within HIV infected and TB-HIV co-infected networks. Raw data was downloaded from Gene Expression Omnibus (GSE50834) and duly pre-processed. Co-expression networks for each condition (HIV infected and TB-HIV co-infected) were constructed independently. Preservation of HIV infected network edges was evaluated with respect to TB-HIV co-infected and vice versa using weighted correlation network analysis. Two out of the 22 modules were identified as exhibiting weak preservation in both conditions. Functional enrichment analysis identified that weakly preserved modules were pertinent to the condition under study. For instance, weakly preserved TBHIV co-infected module T1 enriched for genes associated with mitochondrion exhibited the highest fraction of gene interaction pairs exclusive to TB-HIV co-infection. Concisely, we illustrated the application of using preservation statistics to detect modules functionally linked with dysregulated pathways in disease, as exemplified by the mitochondrion module T1. Our analyses discovered gene clusters that are non-randomly linked with the disease. Highly specific gene pairs pointed to interactions between known markers of disease and favoured identification of possible markers that are likely to be associated with the disease.
Change detection (CD) of remote sensing images aims to identify change areas from bitemporal image pairs. In recent years, with the help of a large amount of remote sensing data and the explosive ...development of artificial intelligence technologies, deep learning methods represented by Convolutional Neural Networks have brought remarkable success in the field of change detection. However, existing methods have difficulty in detecting the detailed change information effectively. In this paper, we propose a siamese differential network with global context enhancement based on the common fully convolution network architecture, namely SDGC-FCN. In the encoding stage, bitemporal images are separately fed into a weight-shared encoder network by differencing feature maps at different scales. In the decoding stage, the difference maps at different levels are concatenated and further processed by a spatial-temporal context enhancement module to highlight the variation regions and obtain significant difference feature maps. Quantitative and qualitative results conducted on LEVIR-CD and WHU-CD datasets demonstrate the effectiveness of our proposed method.
By the graph theory, we first formulate the 1-D wave equation on networks in the form of a vector-valued differential equation in C n . Then we show that its spectra are the same as the zeros of an ...exponential polynomial, which implies that its spectra locate in a strip parallel to the imaginary axis in left half-plane. In the end, we calculate the spectral distribution of a star-shape network using the fortran package ZEAL to demonstrate our theoretical results.
Discovering the pathological mechanism of genetic disease is a challenging task, but has great medical significance. In this paper, a novel method to identify the pathological mechanism of the ...genetic disease was proposed. To validate the validity of the method, as an example, we applied the method to discovery the pathological mechanism of the human Retinitis Pigmentosa by using the gene sequencing data of Retinitis Pigmentosa (RP) and the control group. Firstly, we constructed two locus genotypes interaction networks, which named as the control and the case. Secondly, we compared and analyzed the statistical discrepancy on the proportion and topological properties of nodes between two networks. Finally, this paper discovered one pair of genes, which were closely related to RP (Retinitis Pigmentosa). The biological significance of the results were validated by literature and bioinformatics databases.
Biomarker discovery is one of the major topics in translational biomedicine study based on high-throughput biological data analysis. Traditional methods focus on differentially expressed genes (or ...node-biomarkers) but ignore non-differentials. However, non-differentially expressed genes also play important roles in the biological processes and the rewired interactions / edges among non-differential genes may reveal fundamental difference between variable conditions. Therefore, it is necessary to identify relevant interactions or gene pairs to elucidate the molecular mechanism of complex biological phenomena, e.g. distinguish different phenotypes. To address this issue, we proposed a new method based on a new vector representation of an edge, EdgeMarker, to (1) identify edge-biomarkers, i.e. the differentially correlated molecular pairs (e.g., gene pairs) with optimal classification ability, and (2) transform the ‘node expression’ data in node space into the ‘edge expression’ data in edge space and classify the phenotype of each single sample in edge space, which generally cannot be achieved in traditional methods. Unlike the traditional methods which analyze the node space (i.e. molecular expression space) or higher dimensional space using arbitrary kernel methods, this study provides a mathematical model to explore the edge space (i.e. correlation space) for classification of a single sample. In this work, we show that the identified edge-biomarkers indeed have strong ability in distinguishing normal and disease samples even when all involved genes are not significantly differentially expressed. The analysis of human cholangiocarcinoma dataset and diabetes dataset also suggested that the identified edge-biomarkers may cast new biological insights into the pathogenesis of human complex diseases.
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