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BioData Mining

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https://www.readbyqxmd.com/read/29151892/metrics-to-estimate-differential-co-expression-networks
#1
Elpidio-Emmanuel Gonzalez-Valbuena, Víctor Treviño
Background: Detecting the differences in gene expression data is important for understanding the underlying molecular mechanisms. Although the differentially expressed genes are a large component, differences in correlation are becoming an interesting approach to achieving deeper insights. However, diverse metrics have been used to detect differential correlation, making selection and use of a single metric difficult. In addition, available implementations are metric-specific, complicating their use in different contexts...
2017: BioData Mining
https://www.readbyqxmd.com/read/28912836/methods-for-enhancing-the-reproducibility-of-biomedical-research-findings-using-electronic-health-records
#2
REVIEW
Spiros Denaxas, Kenan Direk, Arturo Gonzalez-Izquierdo, Maria Pikoula, Aylin Cakiroglu, Jason Moore, Harry Hemingway, Liam Smeeth
BACKGROUND: The ability of external investigators to reproduce published scientific findings is critical for the evaluation and validation of biomedical research by the wider community. However, a substantial proportion of health research using electronic health records (EHR), data collected and generated during clinical care, is potentially not reproducible mainly due to the fact that the implementation details of most data preprocessing, cleaning, phenotyping and analysis approaches are not systematically made available or shared...
2017: BioData Mining
https://www.readbyqxmd.com/read/28878825/rna-sequence-data-normalization-through-in-silico-prediction-of-reference-genes-the-bacterial-response-to-dna-damage-as-case-study
#3
Bork A Berghoff, Torgny Karlsson, Thomas Källman, E Gerhart H Wagner, Manfred G Grabherr
BACKGROUND: Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes...
2017: BioData Mining
https://www.readbyqxmd.com/read/28785315/identifying-time-delayed-gene-regulatory-networks-via-an-evolvable-hierarchical-recurrent-neural-network
#4
Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, Scott H Harrison, Abdollah Homaifar
BACKGROUND: The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions...
2017: BioData Mining
https://www.readbyqxmd.com/read/28785314/genetically-improved-barracuda
#5
W B Langdon, Brian Yee Hong Lam
BACKGROUND: BarraCUDA is an open source C program which uses the BWA algorithm in parallel with nVidia CUDA to align short next generation DNA sequences against a reference genome. Recently its source code was optimised using "Genetic Improvement". RESULTS: The genetically improved (GI) code is up to three times faster on short paired end reads from The 1000 Genomes Project and 60% more accurate on a short BioPlanet.com GCAT alignment benchmark. GPGPU BarraCUDA running on a single K80 Tesla GPU can align short paired end nextGen sequences up to ten times faster than bwa on a 12 core server...
2017: BioData Mining
https://www.readbyqxmd.com/read/28785313/nrc-non-coding-rna-classifier-based-on-structural-features
#6
Antonino Fiannaca, Massimo La Rosa, Laura La Paglia, Riccardo Rizzo, Alfonso Urso
MOTIVATION: Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs...
2017: BioData Mining
https://www.readbyqxmd.com/read/28770004/discovery-and-replication-of-snp-snp-interactions-for-quantitative-lipid-traits-in-over-60-000-individuals
#7
Emily R Holzinger, Shefali S Verma, Carrie B Moore, Molly Hall, Rishika De, Diane Gilbert-Diamond, Matthew B Lanktree, Nathan Pankratz, Antoinette Amuzu, Amber Burt, Caroline Dale, Scott Dudek, Clement E Furlong, Tom R Gaunt, Daniel Seung Kim, Helene Riess, Suthesh Sivapalaratnam, Vinicius Tragante, Erik P A van Iperen, Ariel Brautbar, David S Carrell, David R Crosslin, Gail P Jarvik, Helena Kuivaniemi, Iftikhar J Kullo, Eric B Larson, Laura J Rasmussen-Torvik, Gerard Tromp, Jens Baumert, Karen J Cruickshanks, Martin Farrall, Aroon D Hingorani, G K Hovingh, Marcus E Kleber, Barbara E Klein, Ronald Klein, Wolfgang Koenig, Leslie A Lange, Winfried Mӓrz, Kari E North, N Charlotte Onland-Moret, Alex P Reiner, Philippa J Talmud, Yvonne T van der Schouw, James G Wilson, Mika Kivimaki, Meena Kumari, Jason H Moore, Fotios Drenos, Folkert W Asselbergs, Brendan J Keating, Marylyn D Ritchie
BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples...
2017: BioData Mining
https://www.readbyqxmd.com/read/28736578/the-dark-proteome-database
#8
Nelson Perdigão, Agostinho C Rosa, Seán I O'Donoghue
No abstract text is available yet for this article.
2017: BioData Mining
https://www.readbyqxmd.com/read/28694848/epiaco-a-method-for-identifying-epistasis-based-on-ant-colony-optimization-algorithm
#9
Yingxia Sun, Junliang Shang, Jin-Xing Liu, Shengjun Li, Chun-Hou Zheng
BACKGROUND: Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing. RESULTS: In this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm...
2017: BioData Mining
https://www.readbyqxmd.com/read/28694847/arete-candidate-gene-prioritization-using-biological-network-topology-with-additional-evidence-types
#10
Artem Lysenko, Keith Anthony Boroevich, Tatsuhiko Tsunoda
BACKGROUND: Refinement of candidate gene lists to select the most promising candidates for further experimental verification remains an essential step between high-throughput exploratory analysis and the discovery of specific causal genes. Given the qualitative and semantic complexity of biological data, successfully addressing this challenge requires development of flexible and interoperable solutions for making the best possible use of the largest possible fraction of all available data...
2017: BioData Mining
https://www.readbyqxmd.com/read/28674556/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application
#11
Ursula Neumann, Nikita Genze, Dominik Heider
BACKGROUND: Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. RESULTS: The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance...
2017: BioData Mining
https://www.readbyqxmd.com/read/28638442/computational-dynamic-approaches-for-temporal-omics-data-with-applications-to-systems-medicine
#12
REVIEW
Yulan Liang, Arpad Kelemen
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era...
2017: BioData Mining
https://www.readbyqxmd.com/read/28572842/grid-based-stochastic-search-for-hierarchical-gene-gene-interactions-in-population-based-genetic-studies-of-common-human-diseases
#13
Jason H Moore, Peter C Andrews, Randal S Olson, Sarah E Carlson, Curt R Larock, Mario J Bulhoes, James P O'Connor, Ellen M Greytak, Steven L Armentrout
BACKGROUND: Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches...
2017: BioData Mining
https://www.readbyqxmd.com/read/28559929/gene-set-enrichment-analyses-lessons-learned-from-the-heart-failure-phenotype
#14
Vinicius Tragante, Johannes M I H Gho, Janine F Felix, Ramachandran S Vasan, Nicholas L Smith, Benjamin F Voight, Colin Palmer, Pim van der Harst, Jason H Moore, Folkert W Asselbergs
BACKGROUND: Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods. RESULTS: Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are highly sensitive to study design, SNP selection and pruning strategies, SNP-to-gene mapping, and pathway definitions...
2017: BioData Mining
https://www.readbyqxmd.com/read/28546829/vinasse-fertirrigation-alters-soil-resistome-dynamics-an-analysis-based-on-metagenomic-profiles
#15
Lucas P P Braga, Rafael F Alves, Marina T F Dellias, Acacio A Navarrete, Thiago O Basso, Siu M Tsai
Every year around 300 Gl of vinasse, a by-product of ethanol distillation in sugarcane mills, are flushed into more than 9 Mha of sugarcane cropland in Brazil. This practice links fermentation waste management to fertilization for plant biomass production, and it is known as fertirrigation. Here we evaluate public datasets of soil metagenomes mining for changes in antibiotic resistance genes (ARGs) of soils from sugarcane mesocosms repeatedly amended with vinasse. The metagenomes were annotated using the ResFam database...
2017: BioData Mining
https://www.readbyqxmd.com/read/28533819/the-optimal-crowd-learning-machine
#16
Bilguunzaya Battogtokh, Majid Mojirsheibani, James Malley
BACKGROUND: Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness. RESULTS: For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such...
2017: BioData Mining
https://www.readbyqxmd.com/read/28484519/study-of-meta-analysis-strategies-for-network-inference-using-information-theoretic-approaches
#17
Ngoc C Pham, Benjamin Haibe-Kains, Pau Bellot, Gianluca Bontempi, Patrick E Meyer
BACKGROUND: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets...
2017: BioData Mining
https://www.readbyqxmd.com/read/28465724/feature-analysis-for-classification-of-trace-fluorescent-labeled-protein-crystallization-images
#18
Madhav Sigdel, Imren Dinc, Madhu S Sigdel, Semih Dinc, Marc L Pusey, Ramazan S Aygun
BACKGROUND: Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated...
2017: BioData Mining
https://www.readbyqxmd.com/read/28450890/multi-class-computational-evolution-development-benchmark-evaluation-and-application-to-rna-seq-biomarker-discovery
#19
Nathaniel M Crabtree, Jason H Moore, John F Bowyer, Nysia I George
BACKGROUND: A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes...
2017: BioData Mining
https://www.readbyqxmd.com/read/28331548/discovering-feature-relevancy-and-dependency-by-kernel-guided-probabilistic-model-building-evolution
#20
Nestor Rodriguez, Sergio Rojas-Galeano
BACKGROUND: Discovering relevant features (biomarkers) that discriminate etiologies of a disease is useful to provide biomedical researchers with candidate targets for further laboratory experimentation while saving costs; dependencies among biomarkers may suggest additional valuable information, for example, to characterize complex epistatic relationships from genetic data. The use of classifiers to guide the search for biomarkers (the so-called wrapper approach) has been widely studied...
2017: BioData Mining
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