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‣ Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology

Devarajan, Karthik
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorization (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a p×n gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.

‣ Novel opportunities for computational biology and sociology in drug discovery

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
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Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies.

‣ Got target?: computational methods for microRNA target prediction and their extension

Min, Hyeyoung; Yoon, Sungroh
Fonte: Korean Society for Biochemistry and Molecular Biology Publicador: Korean Society for Biochemistry and Molecular Biology
Tipo: Artigo de Revista Científica
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MicroRNAs (miRNAs) are a class of small RNAs of 19-23 nucleotides that regulate gene expression through target mRNA degradation or translational gene silencing. The miRNAs are reported to be involved in many biological processes, and the discovery of miRNAs has been provided great impacts on computational biology as well as traditional biology. Most miRNA-associated computational methods comprise the prediction of miRNA genes and their targets, and increasing numbers of computational algorithms and web-based resources are being developed to fulfill the need of scientists performing miRNA research. Here we summarize the rules to predict miRNA targets and introduce some computational algorithms that have been developed for miRNA target prediction and the application of the methods. In addition, the issue of target gene validation in an experimental way will be discussed.

‣ Computational biology of cardiac myocytes: proposed standards for the physiome

Smith, Nicolas P.; Crampin, Edmund J.; Niederer, Steven A.; Bassingthwaighte, James B.; Beard, Daniel A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /05/2007 Português
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Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.

‣ Perspectives on an Education in Computational Biology and Medicine

Rubinstein, Jill C.
Fonte: YJBM Publicador: YJBM
Tipo: Artigo de Revista Científica
Publicado em 25/09/2012 Português
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The mainstream application of massively parallel, high-throughput assays in biomedical research has created a demand for scientists educated in Computational Biology and Bioinformatics (CBB). In response, formalized graduate programs have rapidly evolved over the past decade. Concurrently, there is increasing need for clinicians trained to oversee the responsible translation of CBB research into clinical tools. Physician-scientists with dedicated CBB training can facilitate such translation, positioning themselves at the intersection between computational biomedical research and medicine. This perspective explores key elements of the educational path to such a position, specifically addressing: 1) evolving perceptions of the role of the computational biologist and the impact on training and career opportunities; 2) challenges in and strategies for obtaining the core skill set required of a biomedical researcher in a computational world; and 3) how the combination of CBB with medical training provides a logical foundation for a career in academic medicine and/or biomedical research.

‣ Highlights from the Third European International Society for Computational Biology (ISCB) Student Council Symposium 2014

Francescatto, Margherita; Hermans, Susanne MA; Babaei, Sepideh; Vicedo, Esmeralda; Borrel, Alexandre; Meysman, Pieter
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 13/02/2015 Português
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In this meeting report, we give an overview of the talks, presentations and posters presented at the third European Symposium of the International Society for Computational Biology (ISCB) Student Council. The event was organized as a satellite meeting of the 13th European Conference for Computational Biology (ECCB) and took place in Strasbourg, France on September 6th, 2014.

‣ Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo; Sorger, Peter Karl
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Background: Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings: We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions: DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature.

‣ A Comparison of Computational Methods for Identifying Virulence Factors

Zheng, Lu-Lu; Li, Yi-Xue; Guo, Xiao-Kui; Feng, Kai-Yan; Wang, Ya-Jun; Hu, Le-Le; Cai, Yu-Dong; Chou, Kuo-Chen; Ding, Juan; Hao, Pei
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
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Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species...

‣ The EM Algorithm and the Rise of Computational Biology

Fan, Xiaodan; Yuan, Yuan; Liu, Jun
Fonte: Institute of Mathematical Statistics Publicador: Institute of Mathematical Statistics
Tipo: Artigo de Revista Científica
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In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the “central dogma” of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.; Statistics

‣ Community-driven development for computational biology at Sprints, Hackathons and Codefests

Möller, Steffen; Afgan, Enis; Banck, Michael; Bonnal, Raoul JP; Booth, Timothy; Chilton, John; Cock, Peter JA; Gumbel, Markus; Harris, Nomi; Holland, Richard; Kalaš, Matúš; Kaján, László; Kibukawa, Eri; Powel, David R; Prins, Pjotr; Quinn, Jacqueli
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
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Background: Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches. Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large. Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together. Results: This report summarises our experience with computational biology Codefests...

‣ Computational approaches for the design and prediction of protein-protein interactions

Grigoryan, Gevorg, Ph. D. Massachusetts Institute of Technology
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Tese de Doutorado Formato: 187 leaves
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There is a large class of applications in computational structural biology for which atomic-level representation is crucial for understanding the underlying biological phenomena, yet explicit atomic-level modeling is computationally prohibitive. Computational protein design, homology modeling, protein interaction prediction, docking and structure recognition are among these applications. Models that are commonly applied to these problems combine atomic-level representation with assumptions and approximations that make them computationally feasible. In this thesis I focus on several aspects of this type of modeling, analyze its limitations, propose improvements and explore applications to the design and prediction of protein-protein interactions.; by Gevorg Grigoryan.; Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2007.; Includes bibliographical references (leaves 167-187).

‣ Computational Methods for Personalized Cancer Therapy Based on Genomics Data; Computergestützte Methoden für die Entwicklung von personalisierten Krebstherapien basierend auf genomischen Daten

Feldhahn, Magdalena
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Dissertação
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Despite the considerable progress in understanding cancer biology and cancer development that has been made over the last decades, the treatment options for cancer are still insufficient. This can be attributed to the tremendous heterogeneity of cancers, with respect to appearance, clinical outcome, and underlying genetic alterations. In traditional concepts of drug design and drug administration, pathologically similar diseases are treated with the same drugs. These approaches are not adequate to face the complexity of cancer. Personalized or individualized approaches, targeting individual characteristics of tumors, are promising concepts to develop successful treatment options for cancer with little side-effects. The human organism is equipped with a powerful system that is capable of targeting abnormal cells specifically and efficiently: the immune system. T cells can distinguish healthy cells from infected or aberrant cells by scanning peptides that are presented on the surface of other cells. Genetic alterations in cancer cells can lead to the presentation of cancer-specific peptides that drive a very specific immune reaction against the cancer cells. These peptides are called cancer-specific T-cell epitopes. Each patient’s immune system is individual with respect to the peptides that can elicit an immune response. The design of tailor-made immunotherapies against individual tumors can thus be realized by using sets of patient- and tumor-specific T-cell epitopes in so-called epitope-based vaccines. A first major challenge in the development of such individualized therapies lies in the analysis of genetic information of individual cancers...

‣ Examining the Use of Homology Models in Predicting Kinase Binding Affinity

Chyan, Jeffrey
Fonte: Universidade Rice Publicador: Universidade Rice
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Drug design is a difficult and multi-faceted problem that has led to extensive interdiscplinary work in the field of computational biology. In recent years, several computational methods have emerged. The overall goal of computational algorithms is to narrow down the number of leads that will be further considered for laboratory experimentation and clinical studies. Much of current drug design focuses on a family of proteins called kinases because they play a pivotal role in many of the cell signaling pathways in the human body. Drugs need to be designed such that they bind to specific kinases in the human kinome inhibiting kinase functions that can be causing various diseases such as cancer. It is important for drugs to have high specificity inhibiting only certain kinases avoiding undesirable effects on the human body. Computational prediction methods can accomplish this complex task by doing a comparative analysis on the binding site of kinases both in sequence and structure to predict binding affinity with potential drugs. However, computational methods depend on existing protein data to make predictions. There is a lack of structural protein data relative to known proteins and protein sequences. A potential solution to the the lack of information is to use computationally generated structural data called homology models. This thesis introduces a framework for the integration of homology models with CCORPS...

‣ Fixed-Parameter Algorithms for the Consensus Analysis of Genomic Data; Festparameter-Algorithmen fuer die Konsens-Analyse Genomischer Daten; Fixed-Parameter Algorithms for the Consensus Analysis of Genomic Data

Gramm, Jens
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: Dissertation; info:eu-repo/semantics/doctoralThesis
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Fixed-parameter algorithms offer a constructive and powerful approach to efficiently obtain solutions for NP-hard problems combining two important goals: Fixed-parameter algorithms compute optimal solutions within provable time bounds despite the (almost inevitable) computational intractability of NP-hard problems. The essential idea is to identify one or more aspects of the input to a problem as the parameters, and to confine the combinatorial explosion of computational difficulty to a function of the parameters such that the costs are polynomial in the non-parameterized part of the input. This makes especially sense for parameters which have small values in applications. Fixed-parameter algorithms have become an established algorithmic tool in a variety of application areas, among them computational biology where small values for problem parameters are often observed. A number of design techniques for fixed-parameter algorithms have been proposed and bounded search trees are one of them. In computational biology, however, examples of bounded search tree algorithms have been, so far, rare. This thesis investigates the use of bounded search tree algorithms for consensus problems in the analysis of DNA and RNA data. More precisely...

‣ The Center for Computational Biology: resources, achievements, and challenges

Toga, Arthur W; Dinov, Ivo D; Thompson, Paul M; Woods, Roger P; Van Horn, John D; Shattuck, David W; Parker, D Stott
Fonte: BMJ Group Publicador: BMJ Group
Tipo: Artigo de Revista Científica
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The Center for Computational Biology (CCB) is a multidisciplinary program where biomedical scientists, engineers, and clinicians work jointly to combine modern mathematical and computational techniques, to perform phenotypic and genotypic studies of biological structure, function, and physiology in health and disease. CCB has developed a computational framework built around the Manifold Atlas, an integrated biomedical computing environment that enables statistical inference on biological manifolds. These manifolds model biological structures, features, shapes, and flows, and support sophisticated morphometric and statistical analyses. The Manifold Atlas includes tools, workflows, and services for multimodal population-based modeling and analysis of biological manifolds. The broad spectrum of biomedical topics explored by CCB investigators include the study of normal and pathological brain development, maturation and aging, discovery of associations between neuroimaging and genetic biomarkers, and the modeling, analysis, and visualization of biological shape, form, and size. CCB supports a wide range of short-term and long-term collaborations with outside investigators, which drive the center's computational developments and focus the validation and dissemination of CCB resources to new areas and scientific domains.

‣ Computational biology: plus c'est la même chose, plus ça change

Huttenhower, Curtis
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
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A report on the joint 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB)/10th Annual European Conference on Computational Biology (ECCB) meetings and the 7th International Society for Computational Biology Student Council Symposium, Vienna, Austria, 15-19 July 2011.

‣ Computational biology: plus c'est la même chose, plus ça change

Huttenhower, Curtis
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Português
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47.05304%
A report on the joint 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB)/10th Annual European Conference on Computational Biology (ECCB) meetings and the 7th International Society for Computational Biology Student Council Symposium, Vienna, Austria, 15-19 July 2011.

‣ Universally Sloppy Parameter Sensitivities in Systems Biology

Gutenkunst, Ryan N.; Waterfall, Joshua J.; Casey, Fergal P.; Brown, Kevin S.; Myers, Christopher R.; Sethna, James P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
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Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a `sloppy' spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements...

‣ MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics

Egea, Jose A; Henriques, David; Cokelaer, Thomas; Villaverde, Alejandro F; Banga, Julio R; Saez-Rodriguez, Julio
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/11/2013 Português
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Optimization is key to solve many problems in computational biology. Global optimization methods provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is limited availability of metaheuristic tools. We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics: enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Both methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at \url{http://www.iim.csic.es/~gingproc/meigo.html}. Documentation and examples are included. The R package has been submitted to Bioconductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology, outperforming other state-of-the-art methods. MEIGO provides a free, open-source platform for optimization...

‣ Module networks revisited: computational assessment and prioritization of model predictions

Joshi, Anagha; De Smet, Riet; Marchal, Kathleen; Van de Peer, Yves; Michoel, Tom
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/01/2009 Português
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The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.; Comment: 8 pages REVTeX...