1,721,369 research outputs found
Statistical analysis of proteomics data: A review on feature selection
No full textThe spread of “-omics” strategies has strongly changed the way of thinking about the scientific method. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach with a data-driven inductive approach, so to generate mechanistical hypotheses from data. Data reduction is a crucial step in the process of proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). In this frame, the aim of the present review article is to give an overview of the methods available for proteomics data analysis, with a focus on biomedical translational research. Suggestions for the choice of the most appropriate standard statistical procedures are presented to perform data reduction by feature selection, cross-validation and functional analysis of proteomics profiles. Significance: The proteome, including all so-called “proteoforms” represents the highest level of complexity of biomolecules when compared to the other “-omes” (i.e., genome, transcriptome). For this reason, the use of proper data reduction strategies is mandatory for proteomics data analysis. However, the strategies to be employed for feature selection must be carefully chosen, since many different approaches exist based on both input data and desired output. So far, a well-established decision-making workflow for proteomics data analysis is lacking, opening up to misleading and incorrect data analysis and interpretation. In this review article many statistical approaches are described and compared for their application in the field of biomedical research, in order to suggest the reader the most suitable analysis pathway and to avoid mistakes
Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence
No full text: Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders
Atti 3° Convegno Nazionale Matematica, Formazione Scientifica e Nuove Tecnologie 9-10-11 dicembre 2004 Macchia di Ferrandina - Matera
No full textAbacavir modulates peroxynitrite-mediated oxidation of ferrous nitrosylated human serum heme-albumin
No full text
Features Selection and Extraction in Statistical Analysis of Proteomics Datasets
No full text“Omics” techniques (e.g., proteomics, genomics, metabolomics), from which huge datasets can nowadays be obtained, require a different way of thinking about data analysis that can be summarized with the idea that, when data are enough, they can speak for themselves. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach (hypothesis-driven) with a data-driven hypothesis-generating inductive approach, so to generate mechanistical hypotheses from data. Data reduction is a crucial step in proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection/extraction methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). Despite big data generated almost daily by proteomics studies, a well-established statistical workflow for data analysis in proteomics is still lacking, opening up to misleading and incorrect data analysis and interpretation. This chapter will give an overview of the methods available for feature selection/extraction in proteomics datasets and how to choose the most appropriate one based on the type of dataset
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
