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Network-based inference

Wei Tang, Zhijun Liao, Quan Zou
MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers' generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages...
October 23, 2016: Oncotarget
Feng Q He, Markus Ollert
Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function...
October 26, 2016: Advances in Biochemical Engineering/biotechnology
Thorsten Rings, Klaus Lehnertz
We investigate the relative merit of phase-based methods for inferring directional couplings in complex networks of weakly interacting dynamical systems from multivariate time-series data. We compare the evolution map approach and its partialized extension to each other with respect to their ability to correctly infer the network topology in the presence of indirect directional couplings for various simulated experimental situations using coupled model systems. In addition, we investigate whether the partialized approach allows for additional or complementary indications of directional interactions in evolving epileptic brain networks using intracranial electroencephalographic recordings from an epilepsy patient...
September 2016: Chaos
Hang Ruan, Zhixi Su, Xun Gu
Recent innovation of RNA-seq technology has shed insightful light on the transcriptomic evolution studies, especially on researches of tissue-specific expression evolution. Phylogenetic analysis of transcriptome data may help to identify causal gene expression differences underlying the evolutionary changes in morphological, physiological, and developmental characters of interest. However, there is a deficiency of software to phylogenetically analyze transcriptome data. To address this need, we have developed an R package TreeExp that can perform comparative expression evolution analysis based on RNA-seq data, which includes optimized input formatting, normalization, pairwise expression distance estimation, expression character tree inference, and preliminary expression phylogenetic network analysis...
October 26, 2016: Journal of Experimental Zoology. Part B, Molecular and Developmental Evolution
Sha Zhu, James H Degnan
Recent work in estimating species relationships from gene trees has included inferring networks assuming that past hybridization has occurred between species. Probabilistic models using the multispecies coalescent can be used in this framework for likelihood-based inference of both network topologies and parameters, including branch lengths and hybridization parameters. A difficulty for such methods is that it is not always clear whether, or to what extent, networks are identifiable - i.e., whether there could be two distinct networks that lead to the same distribution of gene trees...
October 24, 2016: Systematic Biology
Changlong Gu, Bo Liao, Xiaoying Li, Keqin Li
Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases...
October 25, 2016: Scientific Reports
Maryam Songhorzadeh, Karim Ansari-Asl, Alimorad Mahmoudi
Quantifying delayed directional couplings between electroencephalographic (EEG) time series requires an efficient method of causal network inference. This is especially due to the limited knowledge about the underlying dynamics of the brain activity. Recent methods based on information theoretic measures such as Transfer Entropy (TE) made significant progress on this issue by providing a model-free framework for causality detection. However, TE estimation from observed data is not a trivial task, especially when the number of variables is large which is the case in a highly complex system like human brain...
October 14, 2016: Computers in Biology and Medicine
Khursheed Ahmad, Ved P Kumar, Bheem Dutt Joshi, Mohamed Raza, Parag Nigam, Anzara Anjum Khan, Surendra P Goyal
BACKGROUND: The Tibetan antelope (Pantholops hodgsonii), or chiru, is an endangered antelope, distributed in China [Xinjiang, Xizang, Qinghai, Zhuolaihu Lake (Breeding habitat)], and India (Aksai Chin and Ladakh). There is a global demand for the species prized wool, which is used in weaving shahtoosh shawls. Over the years, the population of the Tibetan antelope has drastically declined from more than a million to a few thousand individuals, mainly due to poaching. Field studies undertaken in Ladakh, India also indicated winter migration of the population to Tibet...
October 21, 2016: BMC Research Notes
Alexander Lück, Verena Wolf
BACKGROUND: Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. RESULTS: We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data...
October 21, 2016: BMC Systems Biology
Daifeng Wang, Fei He, Sergei Maslov, Mark Gerstein
Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem's gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally-e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution...
October 2016: PLoS Computational Biology
Yoonsik Shim, Andrew Philippides, Kevin Staras, Phil Husbands
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events...
October 2016: PLoS Computational Biology
Nídia Cangi, Jonathan L Gordon, Laure Bournez, Valérie Pinarello, Rosalie Aprelon, Karine Huber, Thierry Lefrançois, Luís Neves, Damien F Meyer, Nathalie Vachiéry
The disease, Heartwater, caused by the Anaplasmataceae E. ruminantium, represents a major problem for tropical livestock and wild ruminants. Up to now, no effective vaccine has been available due to a limited cross protection of vaccinal strains on field strains and a high genetic diversity of Ehrlichia ruminantium within geographical locations. To address this issue, we inferred the genetic diversity and population structure of 194 E. ruminantium isolates circulating worldwide using Multilocus Sequence Typing based on lipA, lipB, secY, sodB, and sucA genes...
2016: Frontiers in Cellular and Infection Microbiology
Jie Zhou, Pianyu Zhong, Tinghui Zhang
Determination of sequence similarity is one of the major steps in computational phylogenetic studies. One of the major tasks of computational biologists is to develop novel mathematical descriptors for similarity analysis. DNA clustering is an important technology that automatically identifies inherent relationships among large-scale DNA sequences. The comparison between the DNA sequences of different species helps determine phylogenetic relationships among species. Alignment-free approaches have continuously gained interest in various sequence analysis applications such as phylogenetic inference and metagenomic classification/clustering, particularly for large-scale sequence datasets...
2016: Evolutionary Bioinformatics Online
Lianshuo Li, Zicheng Wang, Peng He, Shining Ma, Jie Du, Rui Jiang
Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network is in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network...
October 13, 2016: Genomics, Proteomics & Bioinformatics
Ulrich Nübel
Driven by progress of DNA sequencing technologies, recent population genomics studies have revealed that several bacterial pathogens constitute 'measurably evolving populations'. As a consequence, it was possible to reconstruct the emergence and spatial spread of drug-resistant bacteria on the basis of temporally structured samples of bacterial genome sequences. Based on currently available data, some general inferences can be drawn across different bacterial species as follows: (1) Resistance to various antibiotics evolved years to decades earlier than had been anticipated on the basis of epidemiological surveillance data alone...
October 15, 2016: Current Topics in Microbiology and Immunology
Anatoly Yambartsev, Michael A Perlin, Yevgeniy Kovchegov, Natalia Shulzhenko, Karina L Mine, Xiaoxi Dong, Andrey Morgun
BACKGROUND: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. RESULTS: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches...
October 13, 2016: Biology Direct
Hussein A Hejase, Kevin J Liu
BACKGROUND: Branching events in phylogenetic trees reflect bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study...
October 13, 2016: BMC Bioinformatics
Andrew Abboud, Qi Mi, Ava Puccio, David Okonkwo, Marius Buliga, Gregory Constantine, Yoram Vodovotz
Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days...
2016: Frontiers in Pharmacology
Mona Maneshi, Shahabeddin Vahdat, Jean Gotman, Christophe Grova
Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named "shared and specific independent component analysis" (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i...
2016: Frontiers in Neuroscience
Christopher R Stephens, Constantino González-Salazar, Víctor Sánchez-Cordero, Ingeborg Becker, Eduardo Rebollar-Tellez, Ángel Rodríguez-Moreno, Miriam Berzunza-Cruz, Cristina Domingo Balcells, Gabriel Gutiérrez-Granados, Mircea Hidalgo-Mihart, Carlos N Ibarra-Cerdeña, Martha Pilar Ibarra López, Luis Ignacio Iñiguez Dávalos, María Magdalena Ramírez Martínez
Zoonoses are an important class of infectious diseases. An important element determining the impact of a zoonosis on domestic animal and human health is host range. Although for particular zoonoses some host species have been identified, until recently there have been no methods to predict those species most likely to be hosts or their relative importance. Complex inference networks infer potential biotic interactions between species using their degree of geographic co-occurrence, and have been posited as a potential tool for predicting disease hosts...
October 2016: PLoS Neglected Tropical Diseases
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