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BioData Mining

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https://www.readbyqxmd.com/read/28736578/the-dark-proteome-database
#1
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
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
https://www.readbyqxmd.com/read/28293298/rapid-development-of-entity-based-data-models-for-bioinformatics-with-persistence-object-oriented-design-and-structured-interfaces
#14
Elishai Ezra Tsur
Databases are imperative for research in bioinformatics and computational biology. Current challenges in database design include data heterogeneity and context-dependent interconnections between data entities. These challenges drove the development of unified data interfaces and specialized databases. The curation of specialized databases is an ever-growing challenge due to the introduction of new data sources and the emergence of new relational connections between established datasets. Here, an open-source framework for the curation of specialized databases is proposed...
2017: BioData Mining
https://www.readbyqxmd.com/read/28261328/label-free-data-standardization-for-clinical-metabolomics
#15
Petr G Lokhov, Dmitri L Maslov, Oleg N Kharibin, Elena E Balashova, Alexander I Archakov
BACKGROUND: In metabolomics, thousands of substances can be detected in a single assay. This capacity motivates the development of metabolomics testing, which is currently a very promising option for improving laboratory diagnostics. However, the simultaneous measurement of an enormous number of substances leads to metabolomics data often representing concentrations only in conditional units, while laboratory diagnostics generally require actual concentrations. To convert metabolomics data to actual concentrations, calibration curves need to be generated for each substance, and this process represents a significant challenge due to the number of substances that are present in the metabolomics data...
2017: BioData Mining
https://www.readbyqxmd.com/read/28239419/variant-set-enrichment-an-r-package-to-identify-disease-associated-functional-genomic-regions
#16
Musaddeque Ahmed, Richard C Sallari, Haiyang Guo, Jason H Moore, Housheng Hansen He, Mathieu Lupien
BACKGROUND: Genetic predispositions to diseases populate the noncoding regions of the human genome. Delineating their functional basis can inform on the mechanisms contributing to disease development. However, this remains a challenge due to the poor characterization of the noncoding genome. Here, we propose an R package that can pinpoint which genomic features are etiologically important based on the genetic predispositions. RESULTS: Variant Set Enrichment (VSE) is an R package to calculate the enrichment of a set of disease-associated variants across functionally annotated genomic regions, consequently highlighting the mechanisms important in the etiology of the disease studied...
2017: BioData Mining
https://www.readbyqxmd.com/read/28228844/erratum-to-meta-analytic-support-vector-machine-for-integrating-multiple-omics-data
#17
SungHwan Kim, Jae-Hwan Jhong, JungJun Lee, Ja-Yong Koo
[This corrects the article DOI: 10.1186/s13040-017-0126-8.].
2017: BioData Mining
https://www.readbyqxmd.com/read/28203277/semantics-based-plausible-reasoning-to-extend-the-knowledge-coverage-of-medical-knowledge-bases-for-improved-clinical-decision-support
#18
Hossein Mohammadhassanzadeh, William Van Woensel, Samina Raza Abidi, Syed Sibte Raza Abidi
BACKGROUND: Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge...
2017: BioData Mining
https://www.readbyqxmd.com/read/28191039/elevated-transcriptional-levels-of-aldolase-a-aldoa-associates-with-cell-cycle-related-genes-in-patients-with-nsclc-and-several-solid-tumors
#19
Fan Zhang, Jie-Diao Lin, Xiao-Yu Zuo, Yi-Xuan Zhuang, Chao-Qun Hong, Guo-Jun Zhang, Xiao-Jiang Cui, Yu-Kun Cui
BACKGROUND: Aldolase A (ALDOA) is one of the glycolytic enzymes primarily found in the developing embryo and adult muscle. Recently, a new role of ALDOA in several cancers has been proposed. However, the underlying mechanism remains obscure and inconsistent. In this study, we tried to investigate ALDOA-associated (AA) genes using available microarray datasets to help elucidating the role of ALDOA in cancer. RESULTS: In the dataset of patients with non-small-cell lung cancer (NSCLC, E-GEOD-19188), 3448 differentially expressed genes (DEGs) including ALDOA were identified, in which 710 AA genes were found to be positively associated with ALDOA...
2017: BioData Mining
https://www.readbyqxmd.com/read/28184252/gene-set-analysis-controlling-for-length-bias-in-rna-seq-experiments
#20
Xing Ren, Qiang Hu, Song Liu, Jianmin Wang, Jeffrey C Miecznikowski
BACKGROUND: In gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become an important alternative to traditional expression arrays in gene expression studies. Challenges exist in adopting the existent algorithms to RNA-seq data given the intrinsic difference of the technologies and data...
2017: BioData Mining
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