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1.  An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks 
Advances in Bioinformatics  2013;2013:953814.
The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
doi:10.1155/2013/953814
PMCID: PMC3594945  PMID: 23509452
2.  Literature Retrieval and Mining in Bioinformatics: State of the Art and Challenges 
Advances in Bioinformatics  2012;2012:573846.
The world has widely changed in terms of communicating, acquiring, and storing information. Hundreds of millions of people are involved in information retrieval tasks on a daily basis, in particular while using a Web search engine or searching their e-mail, making such field the dominant form of information access, overtaking traditional database-style searching. How to handle this huge amount of information has now become a challenging issue. In this paper, after recalling the main topics concerning information retrieval, we present a survey on the main works on literature retrieval and mining in bioinformatics. While claiming that information retrieval approaches are useful in bioinformatics tasks, we discuss some challenges aimed at showing the effectiveness of these approaches applied therein.
doi:10.1155/2012/573846
PMCID: PMC3388278  PMID: 22778730
3.  Applications of Natural Language Processing in Biodiversity Science 
Advances in Bioinformatics  2012;2012:391574.
Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.
doi:10.1155/2012/391574
PMCID: PMC3364545  PMID: 22685456
4.  Training Experimental Biologists in Bioinformatics 
Advances in Bioinformatics  2012;2012:672749.
Bioinformatics, for its very nature, is devoted to a set of targets that constantly evolve. Training is probably the best response to the constant need for the acquisition of bioinformatics skills. It is interesting to assess the effects of training in the different sets of researchers that make use of it. While training bench experimentalists in the life sciences, we have observed instances of changes in their attitudes in research that, if well exploited, can have beneficial impacts in the dialogue with professional bioinformaticians and influence the conduction of the research itself.
doi:10.1155/2012/672749
PMCID: PMC3286881  PMID: 22400026
5.  Inferring Biological Mechanisms by Data-Based Mathematical Modelling: Compartment-Specific Gene Activation during Sporulation in Bacillus subtilis as a Test Case 
Advances in Bioinformatics  2012;2011:124062.
Biological functionality arises from the complex interactions of simple components. Emerging behaviour is difficult to recognize with verbal models alone, and mathematical approaches are important. Even few interacting components can give rise to a wide range of different responses, that is, sustained, transient, oscillatory, switch-like responses, depending on the values of the model parameters. A quantitative comparison of model predictions and experiments is therefore important to distinguish between competing hypotheses and to judge whether a certain regulatory behaviour is at all possible and plausible given the observed type and strengths of interactions and the speed of reactions. Here I will review a detailed model for the transcription factor σF, a regulator of cell differentiation during sporulation in Bacillus subtilis. I will focus in particular on the type of conclusions that can be drawn from detailed, carefully validated models of biological signaling networks. For most systems, such detailed experimental information is currently not available, but accumulating biochemical data through technical advances are likely to enable the detailed modelling of an increasing number of pathways. A major challenge will be the linking of such detailed models and their integration into a multiscale framework to enable their analysis in a larger biological context.
doi:10.1155/2011/124062
PMCID: PMC3270535  PMID: 22312331
6.  Systems Biology: The Next Frontier for Bioinformatics 
Advances in Bioinformatics  2011;2010:268925.
Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
doi:10.1155/2010/268925
PMCID: PMC3038413  PMID: 21331364
7.  Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput “omics” Data 
Advances in Bioinformatics  2010;2010:423589.
High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.
doi:10.1155/2010/423589
PMCID: PMC2847380  PMID: 20369061
8.  Evolution and Diversity of the Human Hepatitis D Virus Genome 
Advances in Bioinformatics  2010;2010:323654.
Human hepatitis delta virus (HDV) is the smallest RNA virus in genome. HDV genome is divided into a viroid-like sequence and a protein-coding sequence which could have originated from different resources and the HDV genome was eventually constituted through RNA recombination. The genome subsequently diversified through accumulation of mutations selected by interactions between the mutated RNA and proteins with host factors to successfully form the infectious virions. Therefore, we propose that the conservation of HDV nucleotide sequence is highly related with its functionality. Genome analysis of known HDV isolates shows that the C-terminal coding sequences of large delta antigen (LDAg) are the highest diversity than other regions of protein-coding sequences but they still retain biological functionality to interact with the heavy chain of clathrin can be selected and maintained. Since viruses interact with many host factors, including escaping the host immune response, how to design a program to predict RNA genome evolution is a great challenging work.
doi:10.1155/2010/323654
PMCID: PMC2829689  PMID: 20204073
9.  A Survey of Flow Cytometry Data Analysis Methods 
Advances in Bioinformatics  2009;2009:584603.
Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.
doi:10.1155/2009/584603
PMCID: PMC2798157  PMID: 20049163
10.  A Tutorial of the Poisson Random Field Model in Population Genetics 
Advances in Bioinformatics  2008;2008:257864.
Population genetics is the study of allele frequency changes driven by various evolutionary forces such as mutation, natural selection, and random genetic drift. Although natural selection is widely recognized as a bona-fide phenomenon, the extent to which it drives evolution continues to remain unclear and controversial. Various qualitative techniques, or so-called “tests of neutrality”, have been introduced to detect signatures of natural selection. A decade and a half ago, Stanley Sawyer and Daniel Hartl provided a mathematical framework, referred to as the Poisson random field (PRF), with which to determine quantitatively the intensity of selection on a particular gene or genomic region. The recent availability of large-scale genetic polymorphism data has sparked widespread interest in genome-wide investigations of natural selection. To that end, the original PRF model is of particular interest for geneticists and evolutionary genomicists. In this article, we will provide a tutorial of the mathematical derivation of the original Sawyer and Hartl PRF model.
doi:10.1155/2008/257864
PMCID: PMC2775679  PMID: 19920987

Results 1-10 (10)