bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Prediction of DNA-Recognition by Zinc Finger Proteins
Authors: Sarkar Anita , Kumar Sonu , Punetha Ankita , Grover Abhinav , Sundar Durai
Published: April 2013

Zinc fingers are the most abundant class of DNA-binding proteins encoded in the eukaryotic genomes. Custom-designed zinc finger proteins attached to various DNA-modifying domains can be used to achieve highly specific genome modification, which has tremendous applications in molecular therapeutics. Analysis of sequence and structure of the zinc finger proteins provides clues for understanding protein-DNA interactions and aid in custom-design of zinc finger proteins with tailor-made specificity. Computational methods for prediction of recognition helices for C2H2 zinc fingers that bind to specific target DNA sites could provide valuable insights for researchers interested in designing specific zinc finger proteins for biological and biomedical applications. In this chapter, we describe the zinc finger protein-DNA interaction patterns, challenges in engineering the recognition-specificity of zinc finger proteins, the computational methods of prediction of proteins that recognize specific target DNA sequence and their applications in molecular therapeutics.
Protein Homology Analysis for Function Prediction with Parallel Sub-Graph Isomorphism
Authors: Küçükural Alper , Szilagyi Andras , Sezerman O. Ugur , Zhang Yang
Published: April 2013

To annotate the biological function of a protein molecule, it is essential to have information on its 3D structure. Many successful methods for function prediction are based on determining structurally conserved regions because the functional residues are proved to be more conservative than others in protein evolution. Since the 3D conformation of a protein can be represented by a contact map graph, graph matching, algorithms are often employed to identify the conserved residues in weakly homologous protein pairs. However, the general graph matching algorithm is computationally expensive because graph similarity searching is essentially a NP-hard problem. Parallel implementations of the graph matching are often exploited to speed up the process. In this chapter,the authors review theoretical and computational approaches of graph theory and the recently developed graph matching algorithms for protein function prediction.
Techniques for Named Entity Recognition
Authors: Palshikar Girish Keshav
Published: April 2013

While building and using a fully semantic understanding of Web contents is a distant goal, named entities (NEs) provide a small, tractable set of elements carrying a well-defined semantics. Generic named entities are names of persons, locations, organizations, phone numbers, and dates, while domain-specific named entities includes names of for example, proteins, enzymes, organisms, genes, cells, et cetera, in the biological domain. An ability to automatically perform named entity recognition (NER) – i.e., identify occurrences of NE in Web contents – can have multiple benefits, such as improving the expressiveness of queries and also improving the quality of the search results. A number of factors make building highly accurate NER a challenging task. Given the importance of NER in semantic processing of text, this chapter presents a detailed survey of NER techniques for English text.
Neuronal Transcytosis of WGA Conjugated Protein
Authors: Takeuchi Yoshiki , Matsumoto Yoshiki , Miki Takanori , Warita Katsuhiko , Wang Zhi-Yu , Yakura Tomiko , Liu Jun-Qian
Published: April 2013

Neuronal transcytosis was observed at the stage when no neurotransmitter was released after the injection of wheat germ agglutinin-conjugated horseradish peroxidase (WGA-HRP; WGA = 22 kDa, HRP = 40 kDa) into the vagus nerve. The co-injection of Rab3A-siRNA with WGA-HRP into the vagus nerve was performed to further examine this phenomenon. This co-injection resulted in the transcytosis of WGA-HRP, both of the passing type, by which it crossed the synapses, and of the secretion type followed by endocytosis of postsynaptic membranes. These findings raised the possibility in vivo that WGA plays an important role in the transcytosis of protein. Therefore, WGA may be a valuable tool for therapeutic drug targeting via transcytosis. The ability of WGA-conjugated Amyloid ß (WGA-Aß) to decrease amyloid deposits in Alzheimer’s disease was investigated. The conjugation of WGA to amyloid-ß (1-40) (Aß; 5 kDa) was confirmed. WGA-Aß was then shown to move to terminals by axonal flow in vivo as well as WGA-HRP. WGA-Aß was also observed in the nodose ganglion cells and terminals after injections of fluorescent Aß (FAß) into the vagus nerve and fluorescent WGA (FWGA) into the common carotid artery. These studies suggested that WGA-Aß could be localized to solitary neurons via transcytosis.
Graph Mining Techniques
Authors: Appel Ana Paula , Faloutsos Christos , Junior Caetano Traina
Published: April 2013

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A large amount of graph patterns, as well as graph generator models that mimic such patterns have been proposed over the last years. However, a deep and recurring question still remains: “What is a good pattern?” The answer is related to finding a pattern or a tool able to help distinguishing between actual real-world and fake graphs. Here we explore the ability of ShatterPlots, a simple and powerful algorithm to tease out patterns of real graphs, helping us to spot fake/masked graphs. The idea is to force a graph to reach a critical (“Shattering”) point, randomly deleting edges, and study its properties at that point.
Biclustering of DNA Microarray Data
Authors: Tchagang Alain B. , Pan Youlian , Famili Fazel , Tewfik Ahmed H. , Benos Panayiotis V.
Published: April 2013

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.
Unsupervised Methods to Identify Cellular Signaling Networks from Perturbation Data
Authors: Natarajan Madhusudan
Published: April 2013

The inference of cellular architectures from detailed time-series measurements of intracellular variables is an active area of research. High throughput measurements of responses to cellular perturbations are usually analyzed using a variety of machine learning methods that typically only work within one type of measurement. Here, summaries of some recent research attempts are presented–these studies have expanded the scope of the problem by systematically integrating measurements across multiple layers of regulation including second messengers, protein phosphorylation markers, transcript levels, and functional phenotypes into signaling vectors or signatures of signal transduction. Data analyses through simple unsupervised methods provide rich insight into the biology of the underlying network, and in some cases reconstruction of key architectures of the underlying network from perturbation data. The methodological advantages provided by these efforts are examined using data from a publicly available database of responses to systematic perturbations of cellular signaling networks generated by the Alliance for Cellular Signaling (AfCS).
Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis
Authors: Yu Jian , Wu Jun , Li Miaoxin , Yi Yajun , Shyr Yu , Xie Lu
Published: April 2013

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.
Modules in Biological Networks
Authors: Zhang Bing , Shi Zhiao
Published: April 2013

One of the most prominent properties of networks representing complex systems is modularity. Network-based module identification has captured the attention of a diverse group of scientists from various domains and a variety of methods have been developed. The ability to decompose complex biological systems into modules allows the use of modules rather than individual genes as units in biological studies. A modular view is shaping research methods in biology. Module-based approaches have found broad applications in protein complex identification, protein function prediction, protein expression prediction, as well as disease studies. Compared to single gene-level analyses, module-level analyses offer higher robustness and sensitivity. More importantly, module-level analyses can lead to a better understanding of the design and organization of complex biological systems.
Complexity and Modularity of MAPK Signaling Networks
Authors: Popescu George V. , Popescu Sorina C.
Published: April 2013

Signaling through mitogen-activated protein kinase (MAPK) cascades is a conserved and fundamental process in all eukaryotes. This chapter reviews recent progress made in the identification of components of MAPK signaling networks using novel large scale experimental methods. It also presents recent landmarks in the computational modeling and simulation of the dynamics of MAPK signaling modules. The in vitro MAPK signaling network reconstructed from predicted phosphorylation events is dense, supporting the hypothesis of a combinatorial control of transcription through selective phosphorylation of sets of transcription factors. Despite the fact that additional co-factors and scaffold proteins may regulate the dynamics of signal transduction in vivo, the complexity of MAPK signaling networks supports a new model that departs significantly from that of the classical definition of a MAPK cascade.
Using Systems Biology Approaches to Predict New Players in the Innate Immune System
Authors: Li Bin
Published: April 2013

Toll-like receptors (TLRs) are critical players in the innate immune response to pathogens. However, transcriptional regulatory mechanisms in the TLR activation pathways are still relatively poorly characterized. To address this question, the author of this chapter applied a systematic approach to predict transcription factors that temporally regulate differentially expressed genes under diverse TLR stimuli. Time-course microarray data were selected from mouse bone marrow-derived macrophages stimulated by six TLR agonists. Differentially regulated genes were clustered on the basis of their dynamic behavior. The author then developed a computational method to identify positional overlapping transcription factor (TF) binding sites in each cluster, so as to predict possible TFs that may regulate these genes. A second microarray dataset, on wild-type, Myd88-/- and Trif-/- macrophages stimulated by lipopolysaccharide (LPS), was used to provide supporting evidence on this combined approach. Overall, the author was able to identify known TLR TFs, as well as to predict new TFs that may be involved in TLR signaling.
A Two-Layer Learning Architecture for Multi-Class Protein Folds Classification
Authors: Wang Ruofei , Gao Xieping
Published: April 2013

Classification of protein folds plays a very important role in the protein structure discovery process, especially when traditional sequence alignment methods fail to yield convincing structural homologies. In this chapter, we have developed a two-layer learning architecture, named TLLA, for multi-class protein folds classification. In the first layer, OET-KNN (Optimized Evidence-Theoretic K Nearest Neighbors) is used as the component classifier to find the most probable K-folds of the query protein. In the second layer, we use support vector machine (SVM) to build the multi-class classifier just on the K-folds, generated in the first layer, rather than on all the 27 folds. For multi-feature combination, ensemble strategy based on voting is selected to give the final classification result. The standard percentage accuracy of our method at ~63% is achieved on the independent testing dataset, where most of the proteins have <25% sequence identity with those in the training dataset. The experimental evaluation based on a widely used benchmark dataset has shown that our approach outperforms the competing methods, implying our approach might become a useful vehicle in the literature.
Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs
Authors: Moschopoulos Charalampos , Beligiannis Grigorios , Likothanassis Spiridon , Kossida Sophia
Published: April 2013

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.
Kernel Generative Topographic Mapping of Protein Sequences
Authors: Cárdenas Martha-Ivón , Vellido Alfredo , Olier Iván , Rovira Xavier , Giraldo Jesús
Published: April 2013

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligent data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-coupled receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.
Bifurcation Analysis of a Model Accounting for the 14-3-3s Signalling Compartmentalisation
Authors: Nikolov S. , Vera J. , Wolkenhauer O.
Published: April 2013

Bifurcation theory studies the qualitative changes in the phase portrait when we vary the parameters of the system. In this book chapter we adapt and extend a mathematical model accounting for the subcellular localisation of 14-3-3s, a protein involved in cell cycle arrest and the regulation of apoptosis. The model is analysed with analytical tools coming from Lyapunov-Andronov theory, and our analytical calculations predict that soft (reversible) loss of stability takes place.
Computational Sequence Design Techniques for DNA Microarray Technologies
Authors: Tulpan Dan , Ghiggi Athos , Montemanni Roberto
Published: April 2013

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.
In Silico Pharmaco-Gene-Informatic Identification of Insulin-Like Proteins in Plants
Authors: Jyothi Koona Saradha , Sridhar G. R. , Srinivas Kudipudi , Rao B. Subba , Apparao Allam
Published: April 2013

This chapter presents an extension of the authors’ earlier work, where they showed that nucleotide/amino acid sequences related to insulin occurred in the plant kingdom. It was believed that plants did not have, nor did they need insulin, a protein hormone considered to be restricted to the animal kingdom. In the current study, the human insulin sequence was initially obtained from UniProt/SwissProt (accession no. P01308). Plant genome sequences were obtained from NCBI PubMed (Bauhinia purpurea [Gi|229412], Vigna unguiculata [P83770], and Canavalia ensiformis [Gi|7438602]. Scores were obtained from ProtFun 2.2 [http://www.cbs.dtu.dk/services/ProtFun/]. At the next stage, functions of insulin and glucokinin (insulin like proteins in plants) were predicted by the Protein Function Prediction database (http://dragon.bio.purdue.edu/pfp/index.html), followed by functional site prediction from the ELM database (http://elm.eu.org/). ProtFun predicted the following functions: human insulin (Cell envelope), Jack bean (Energy metabolism), Bauhinia purpurea(Translation). The amino acid Glycine at 32 positions was most highly conserved. Present predictions advocate the use of these sequences (QHLCGS motif) as targets for probing the other plants with lesser homology. In summary our in silico studies have suggested that Bauhinia purpurea (Purple orchid tree-BP), Vigna unguiculata (Cow pea-CP) and Canavalia ensiformis (Jack bean-JB) have conserved the important regions of the human insulin protein.
Molecular Network Analysis of Target RNAs and Interacting Proteins of TDP-43, a Causative Gene for the Neurodegenerative Diseases ALS/FTLD
Authors: Satoh Jun-Ichi
Published: April 2013

TAR DNA-binding protein-43 (TDP-43) is an evolutionarily conserved nuclear protein that regulates gene expression by forming a multimolecular complex with a wide variety of target RNAs and interacting proteins. Abnormally phosphorylated, ubiquitinated, and aggregated TDP-43 proteins constitute a principal component of neuronal and glial cytoplasmic and nuclear inclusions in the brains of patients with amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), establishing a novel clinical entity designated TDP-43 proteinopathy. Although increasing evidence suggests that the neurodegenerative process underlying ALS and FTLD is attributable to a toxic gain of function or a loss of cellular function of TDP-43, the precise molecular mechanisms remain largely unknown. Recent advances in systems biology enable us to characterize the global molecular network extracted from large-scale data of the genome, transcriptome, and proteome with the pathway analysis tools of bioinformatics endowed with a comprehensive knowledge base. The present study was conducted to characterize the comprehensive molecular network of TDP-43 target RNAs and interacting proteins, recently identified by deep sequencing with next-generation sequencers and mass spectrometric analysis. The results propose the systems biological view that TDP-43 serves as a molecular coordinator of the RNA-dependent regulation of gene transcription and translation pivotal for performing diverse neuronal functions and that the disruption of TDP-43-mediated molecular coordination induces neurodegeneration in ALS and FTLD.
Improving Prediction Accuracy via Subspace Modeling in a Statistical Geometry Based Computational Protein Mutagenesis
Authors: Masso Majid
Published: April 2013

A computational mutagenesis is detailed whereby each single residue substitution in a protein chain of primary sequence length N is represented as a sparse N-dimensional feature vector, whose M << N nonzero components locally quantify environmental perturbations occurring at the mutated position and its neighbors in the protein structure. The methodology makes use of both the Delaunay tessellation algorithm for representing protein structures, as well as a four-body, knowledge based, statistical contact potential. Feature vectors for each subset of mutants due to all possible residue substitutions at a particular position cohabit the same M-dimensional subspace, where the value of M and the identities of the M nonzero components are similarly position dependent. The approach is used to characterize a large experimental dataset of single residue substitutions in bacteriophage T4 lysozyme, each categorized as either unaffected or affected based on the measured level of mutant activity relative to that of the native protein. Performance of a single classifier trained with the collective set of mutants in N-space is compared to that of an ensemble of position-specific classifiers trained using disjoint mutant subsets residing in significantly smaller subspaces. Results suggest that significant improvements can be achieved through subspace modeling.
Predictive Dynamical Modelling MicroRNAs Role in Complex Networks
Authors: Nikolova Elena V. , Herwig Ralf , Nikolov Svetoslav G. , Petrov Valko G.
Published: April 2013

The aim of this chapter is to give an extended analytical consideration of mathematical modelling of the microRNA role in cancer networks. For this purpose, ordinary and partial differential equations are used for synthesizing and analyzing the models of gene, microRNAs and mRNAs concentration alterations as time-dependent variables related by functional and differential relations. The architecture of the models and the definitions of their components are inspired by the qualitative theory of differential equations. This chapter’s analysis shows that it is able to ensure the authenticity and validity of the following qualitative conclusions: (a) the rates of protein production decrease with the increasing constant production rate of microRNA at microRNA-mediated target regulation on mRNAs; (b) time delay has a stabilizing role in the interaction between the miRNA-17-92 cluster and the transcription factors E2F and Myc.
Decision Support Systems in the Process of Improving Patient Safety
Authors: Kalina Jan , Zvárová Jana
Published: April 2013

The chapter presents decision support systems in medicine, their basic principles, and structure. From the point of view of patient safety, the decision support systems can bring new unexpected sources of errors, which must be anticipated at the design, implementation, and validation stages. Nevertheless, a safe and easy-to-use system can greatly improve the quality of determining the diagnosis, prognosis, and therapy in healthcare. The authors of this chapter concentrate on the contribution of decision support systems to patient safety and on their potential to future contributions. A decision support system requires a user-friendly interface with the electronic health record and information system within the healthcare facility. The authors also present two examples of decision support systems from the genetics research.
Visualization of Protein 3D Structures in ‘Double-Centroid’ Reduced Representation
Authors: Reyes Vicente M. , Sheth Vrunda
Published: April 2013

This article is of two parts: (a) the development of a protein reduced representation and its implementation in a Web server; and (b) the use of the reduced protein representation in the modeling of the binding site of a given ligand and the screening for the model in other protein 3D structures. Current methods of reduced protein 3D structure representation such as the Ca trace method not only lack essential molecular detail, but also ignore the chemical properties of the component amino acid side chains. This chapter describes a reduced protein 3D structure representation called “double-centroid reduced representation” and presents a visualization tool called the “DCRR Web Server” that graphically displays a protein 3D structure in DCRR along with non-covalent intra- and intermolecular hydrogen bonding and van der Waals interactions. In the DCRR model, each amino acid residue is represented as two points: the centroid of the backbone atoms and that of the side chain atoms; in the visualization Web server, they and the non-bonded interactions are color-coded for easy identification. The visualization tool in this chapter is implemented in MATLAB and is the first for a reduced protein representation as well as one that simultaneously displays non-covalent interactions in the molecule. The DCRR model reduces the atomicity of the protein structure by ~75% while capturing the essential chemical properties of the component amino acids. The second half of this chapter describes the application of this reduced representation to the modeling and screening of ligand binding sites using a data model termed the “tetrahedral motif.” This type of ligand binding site modeling and screening presents a novel type of pharmacophore modeling and screening, one that depends on a reduced protein representation.
JFeature
Authors: Chen Xin , Xu Hangyang
Published: April 2013

Prediction of various functional properties of proteins has long been a central theme of bioinformatics in the post-genomic era. Statistical learning, in addition to analysis based on sequence similarity, was proven successful to detect complex sequence-function associations in many applications. JFeature is an integrated Java tool to facilitate extraction of global sequence features and preparation of example sets, in statistical learning studies of sequence-function relationships. With a user-friendly graphical interface, it computes the composition, distribution, transition and auto-correlation features from sequence. It also helps to assemble a negative example set based on the most-dissimilar principle. The Java package and supplementary documentations are available at http://www.cls.zju.edu.cn/rlibs/software/jfeature.html.
Evaluation of Coupled Nuclear and Cytoplasmic p53 Dynamics
Authors: Sun Tingzhe , Cai Meihong , Cui Jun , Shen Pingping
Published: April 2013

The tumor suppressor protein p53 predominantly serves as a sequence specific transcription factor that may be activated upon exposure to diverse stimuli. One potent death inducer, p53-upregulated mediator of apoptosis (PUMA), is transcriptionally induced by p53. Once released into the cytoplasm, PUMA can lead to the activation of Bcl-2 apoptotic network. The cytoplasmic proapoptotic roles of p53 have recently been discovered, and these findings have placed p53 into the chemical interaction network with Bcl-2 family members. PUMA can also relieve p53 from the sequestration of antiapoptotic members. Released p53 further enters the nucleus and induces PUMA expression. We proposed that this positive feedback loop could lead to bistability. Further sensitivity analysis suggested that the system which covers the interactions between p53 and BCL-2 family members is considerably sensitive to p53 production rate. Meanwhile, downstream network components are much more affected by certain parameters than upstream effectors. Therefore, this newly discovered positive feedback loop might play critical roles in apoptotic network.
Data Mining and Meta-Analysis on DNA Microarray Data
Authors: Paparountas Triantafyllos , Nikolaidou-Katsaridou Maria Nefeli , Rustici Gabriella , Aidinis Vasilis
Published: April 2013

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.
Efficient Computational Construction of Weighted Protein-Protein Interaction Networks Using Adaptive Filtering Techniques Combined with Natural Selection-Based Heuristic Algorithms
Authors: Dimitrakopoulos Christos M. , Theofilatos Konstantinos A. , Georgopoulos Efstratios F. , Likothanassis Spiridon , Tsakalidis Athanasios , Mavroudi Seferina P.
Published: April 2013

The analysis of protein-protein interactions (PPIs) is crucial to the understanding of cellular processes. In recent years, a variety of computational methods have been developed to supplement the interactions that have been detected experimentally. The article’s main objective is to present a novel classification framework for predicting PPIs combining the advantages of two algorithmic methods’ categories. State-of-the-art adaptive filtering techniques were combined with the most contemporary heuristic methods which are based in the natural selection process. The authors’ goal is to find a simple mathematical equation that governs the best classifier enabling the extraction of biological knowledge. The proposed methodology assigns a confidence score to each protein pair and as a result a weighted PPI network is constructed. All possible combinations of the selected adaptive filtering and heuristic techniques were used and comparisons were made to explore the classifiers with the highest performance and interpretability.
BMP Signaling in Regenerative Medicine
Authors: Zimmer Julia , Degenkolbe Elisa , Wildemann Britt , Seemann Petra
Published: April 2013

More than 40 years after the discovery of Bone Morphogenetic Proteins (BMPs) as bone inducers, a whole protein family of growth factors connected to a wide variety of functions in embryonic development, homeostasis, and regeneration has been characterized. Today, BMP2 and BMP7 are already used in the clinic to promote vertebral fusions and restoration of non-union fractures. Besides describing present clinical applications, the authors review ongoing trials highlighting the future possibilities of BMPs in medicine. Apparently, the physiological roles of BMPs have expanded their range from bone growth induction and connective tissue regeneration to cancer diagnosis/treatment and cardiovascular disease prevention.
A Comparative Study of an Unsupervised Word Sense Disambiguation Approach
Authors: Xiong Wei , Song Min , deVersterre Lori
Published: April 2013

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms: support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17%, respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL’s clustering technique.
The Impact of Genetic Testing and Genetic Information on Ethical, Legal and Social Issues in North America
Authors: Serenko Natalia
Published: April 2013

This chapter discusses the impact of genetic testing and genetic information. It proposes a framework that facilitates a critical analysis of the ethical, legal, and social issues of genetic testing. The ethical effects include privacy infringement, genetic discrimination, misleading advertisement, psychological impact, and individual autonomy. The legal impacts embrace consistent terminology, referral guidelines, patent wars, and new legislations. The social effects pertain to inequality, higher insurance fees, tax burden, and fear of new eugenics. Information and communication technologies dramatically augment the effect of genetic testing on these outcomes. This chapter argues that information and communication technologies and rapid advances in genetics challenge the existing legislation systems in North America. Therefore, policy-makers need to address the tension between the potential benefits and harms of genetic testing and genetic information.
A Philosophical Exploration of the Concept of ‘Property’ in Genetics and Databanking
Authors: Döring Ole
Published: April 2013

The chapter criticizes arguments purporting to show that the human body could be made available in the market as property and those arguing that the concept of property could be applicable to the human bodily parts or human DNA. The author argues that the genetic information contained in matter such as DNA cannot be taken for granted as classifiable as property. There are three reasons: DNA is too personal to be commodified; DNA is of familial nature; and commercialization of DNA runs the risk of exploitation of the disadvantaged. Moreover, ethics should venture to clarify interests and stakes in the debate, with sympathy for the vulnerable rather than executing the rationales of powerful groups in economy and society.
Ethics, Privacy, and the Future of Genetic Information in Healthcare Information Assurance and Security
Authors: Springer John A. , Beever Jonathan , Morar Nicolae , Sprague Jon E. , Kane Michael D.
Published: April 2013

The risks associated with the misuse and abuse of genetic information are high, as the exploitation of an individual’s genetic information represents the ultimate example of identity theft. Hence, as the frontline of defense, information assurance and security (IAS) practitioners must be intimately familiar with the multidimensional aspects surrounding the use of genetic information in healthcare. To achieve that aim, this chapter addresses the ethical, privacy, economic, and legal aspects of the future uses of genetic information in healthcare and discusses the impact of these uses on IAS. The reader gains an effective ethical framework in which to understand and evaluate the competing demands placed upon the IAS practitioners by the transformative utility of genomics.
Risk-Benefit Analysis of Combination vs. Unopposed HRT in Post-Menopausal Women
Authors: Ishaq Geer Mohammad , Hussain Pz Tasaduq , Iqbal Mir Javid , Mushtaq Mohsin Bin
Published: April 2013

Many trials on the use of hormone replacement therapy (HRT) have provided contradictory results on its risks and benefits in post-menopausal women. The use of HRT declined globally following publication of the first data from the Women’s Health Initiative (WHI) trial in 2002, with the revelation that there was an increased risk of breast cancer and coronary heart disease (CHD) in postmenopausal women taking HRT. Following this, other leading studies published results that were consistent with these findings, which reduced enthusiasm for HRT use. However, recent publications from the International Menopause Society indicate that HRT is the first-line and most effective treatment for menopausal symptoms. Moreover, when the full results of the WHI trial were subsequently published, it appeared that HRT may confer benefits for CHD prevention below age 60. The statements from the British Menopause Society and the International Menopause Society (IMS) published in 2008 also supported this opinion. These revelations renew interest in HRT use. This paper analyzes the effects of combination versus unopposed HRT on osteoporosis, breast and CHD, endometrial cancer induction, venous thromboembolic disease, lipids and lipoproteins, neuroprotection, and cognitive function in post-menopausal women.
Plastika [Totipotenta]
Authors: Nyeki Catherine
Published: April 2013

Over the last ten years, my work has developed into a biotope abounding in diversity, a sort of virtual “vivarium” composed of interactive pieces, musical creations, drawings and writings in numerous sketchbooks, films and installations. A digital work of art is by definition non-uniqueness; it is potentially transformable at any moment. It is both fascinating and intriguing to have access to such a “plastic material”. My artistic research, which has flexible boundaries and echoes that of today’s nanosciences, genetics and cell biology, has gradually led me to improve certain personal concepts like the sensitive microscope, tactile laboratory, imaginary incubator, parallel botany, nano evolutions, visible and audible strata, multilingual semantic zoo, bud cell, virtual tissue, body graft… and the list goes on. My contribution’s title, Plastika [Totipotenta], is taken from my last solo exhibition in Paris that brought together recent works related to plasticity, questioning a sort of constantly evolving “modeling clay”-type thinking. This chapter is an invitation to question the various levels of plasticity concepts applied to some of my latest works that have been inspired by current biotechnology and my recent collaboration with a cell biologist.
Pharmacovigilance
Authors: Jose Jimmy
Published: April 2013

Any substance that is capable of producing a therapeutic effect can also produce unwanted or adverse effects. It is important to understand the basic concepts related to Adverse Drug Reactions (ADRs): epidemiology, classification, predisposing factors, evaluation parameters, and surveillance methods. Pharmacovigilance is defined as the science and activities relating to the detection, evaluation, understanding, and prevention of ADRs or any other drug-related problems. It involves patients, medical professionals, the pharmaceutical industry, drug regulatory agencies, and academic scientists. Pharmacoinformatics, the application of information technology with regard to the drug design, development, and drug use has played a major role in the appropriate implementation of pharmacovigilance at industry, regulatory, and hospital levels. The functioning of international regulatory agencies and drug safety departments of pharmaceutical industries has been greatly influenced by pharmacoinformatics. Pharmacoinformatics has changed the way in which health care is practiced. Modern information technology can be used by health care professionals for various purposes and, thereby, make a substantial contribution to optimize the quality of medication use in institutions with due importance of safety. Pharmacoinformatics has a major influence in the development of pharmacogenetics and its individual applications including improving drug safety. Pharmacoinformatics will play a major role in the future development and practice of pharmacovigilance. The present chapter is aimed at providing the readers an insight into the importance and basic concepts of pharmacovigilance, and the process involved in it. Application of pharmacoinformatics in improving drug safety at various levels from an industry, regulatory and hospital perspective is discussed.
Language Focus for Genetics and Molecular Biology Students
Authors: Lidbury Brett Andrew
Published: April 2013

This chapter examines the role of scientific language comprehension and confidence for senior undergraduate students in Genetics and Molecular Biology, and the impact of language-centred learning strategies to assessment outcomes. A number of online and tutorial language exercises and strategies are described that were designed to promote scientific language competence and subsequent genetics learning. The effect of these interventions was analysed through grade and assessment performance comparisons with earlier traditionally taught Genetics cohorts. While no significant grade improvements were found for cohorts taught via language, deeper statistical analysis revealed that motivation to adopt new learning strategies was crucial for best student performance. Language was found to be most influential for middle range performing students. Despite at least a year of tertiary education, students still had difficulty interpreting some everyday words in a science context. The study also encourages a greater evaluation of student motivation in adopting new learning techniques.
Data Analysis and Interpretation in Metabolomics
Authors: Garcia-Manteiga Jose M.
Published: April 2013

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.
Aß Monomer, Oligomer and Fibril in Alzheimer’s Disease
Authors: Mori Hiroshi
Published: April 2013

Alzheimer’s disease (AD), the most prevalent disease of aged people, is a progressive neurodegenerative disorder with dementia. Amyloid-ß (also known as ß-protein and referred to here as Aß) is a well-established, seminal peptide in AD that is produced from the amyloid precursor protein (APP) by consecutive digestion with the ß secretase of BACE (beta-site amyloid cleaving enzyme) and gamma secretase of the presenilin complex. Abnormal cerebral accumulation of Abeta in the form of insoluble fibrils in senile plaques and cerebral amyloid angiopathy (CAA) is a neuropathological hallmark of AD. In contrast to insoluble fibrillary Aß, a soluble oligomeric complex, ADDL, consists of low-n oligomers of Aß, such as Aß*56. Despite their different names, it is currently proposed that oligomeric Aß is directly involved in synaptic toxicity and cognitive dysfunction in the early stages of AD. This chapter identifies a novel APP mutation (E693delta; referred to as the Osaka mutation) in a pedigree with probable AD, resulting in a variant Aß lacking glutamate at position 22. Based on theoretical predictions and in vitro studies on synthetic mutant Aß peptides, the mutated Aß peptide showed a unique and enhanced oligomerization activity without fibrillization. This was further confirmed by PiB-PET analysis on the proband patient. Collectively, the chapter concludes that the Osaka mutation is the first human evidence for the hypothesis that oligomeric Aß is involved in AD.
BTISNet
Authors: Kandpal K. N. , Khan Mohammad Faheem , Rawat S. S.
Published: April 2013

Bioinformatics plays an important role in speeding up the research in the field of biotechnology related to plant and animal science. A bioinformatics program under BTISNet has been catering to the information needs of ten-twelve thousand scientific personnel. The Apex Centre at the Department of Biotechnology, New Delhi, along with fifty-six centers and thirty-one Bioinformatics Infrastructure Facility Centres (BIFs) are engaged in this task to serve researchers, scientists, students, etc.
Intelligent Classifiers Fusion for Enhancing Recognition of Genes and Protein Pattern of Hereditary Diseases
Authors: Subhasini Parthasarathy , Ane Bernadetta Kwintiana , Roller Dieter , Krishnaveni Marimuthu
Published: April 2013

Most the objective of intelligent systems is to create a model, which given a minimum amount of input data or information, is able to produce reliable recognition rates and correct decisions. In the application, when an individual classifier has reached its limit and, at the same time, it is hard to develop a better one, the solution might only be to combine the existing well performing classifiers. Combination of multiple classifier decisions is a powerful method for increasing classification rates in difficult pattern recognition problems. To achieve better recognition rates, it has been found that in many applications, it is better to fuse multiple relatively simple classifiers than to build a single sophisticated classifier. Such classifiers fusion seems to be worth applying in terms of uncertainty reduction. Different individual classifiers performing on different data would produce different errors. Assuming that all individual methods perform well, intelligent combination of multiple experts would reduce overall classification error and as consequence increase correct outputs. To date, content interpretation still remains as a highly complex task which requires many features to be fused. However, the fusion mechanism can be done at different levels of the classification. The fusion process can be carried out on three levels of abstraction closely connected with the flow of the classification process, i.e. data level fusion, feature level fusion, and classifier fusion. The work presented in this chapter focuses on the fusion of classifier outputs for intelligent models.
Mining Statistically Significant Substrings based on the Chi-Square Measure
Authors: Dutta Sourav , Bhattacharya Arnab
Published: April 2013

With the tremendous expansion of reservoirs of sequence data stored worldwide, efficient mining of large string databases in various domains including intrusion detection systems, player statistics, texts, and proteins, has emerged as a practical challenge. Searching for an unusual pattern within long strings of data is one of the foremost requirements for many diverse applications. Given a string, the problem is to identify the substrings that differ the most from the expected or normal behavior, i.e., the substrings that are statistically significant (or, in other words, less likely to occur due to chance alone). We first survey and analyze the different statistical measures available to meet this end. Next, we argue that the most appropriate metric is the chi-square measure. Finally, we discuss different approaches and algorithms proposed for retrieving the top-k substrings with the largest chi-square measure.