65 research outputs found
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2â7 July 201
Three Risky Decades: A Time for Econophysics?
Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trappedâthe current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be builtâonly on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era
A novel approach to infer orthologs and produce gene annotations at scale
Aufgrund von Fortschritten im Bereich der DNA-Sequenzierung hat die Anzahl verfĂźgbarer Genome in den letzten Jahrzehnten rapide zugenommen. Tausende bereits heute zur VerfĂźgung stehende Genome ermĂśglichen detaillierte vergleichende Analysen, welche fĂźr die Beantwortung relevanter Fragestellungen essentiell sind. Dies betrifft die Assoziation von Genotyp und Phänotyp, die Erforschung der Besonderheiten komplexer Proteine und die Weiterentwicklung medizinischer Anwendungen. Um all diese Fragen zu beantworten ist es notwendig, proteinkodierende Gene in neu sequenzierten Genomen zu annotieren und ihre Homologieverhältnisse zu bestimmen. Die bestehenden Methoden der Genomanalyse sind jedoch nicht fĂźr Menge heutzutage anfallender Datenmengen ausgelegt. Daher ist die zentrale Herausforderung in der vergleichenden Genomik nicht die Anzahl der verfĂźgbaren Genome, sondern die Entwicklung neuer Methoden zur Datenanalyse im Hochdurchsatz. Um diese Probleme zu adressieren, schlage ich ein neues Paradigma der Annotation von Genomen und der Inferenz von Homologieverhältnissen vor, welches auf dem Alignment gesamter Genome basiert. Während die derzeit angewendeten Methoden zur Gen-Annotation und Bestimmung der Homologie ausschlieĂlich auf codierenden Sequenzen beruhen, kĂśnnten durch die Einbeziehung des umgebenden neutral evolvierenden genomischen Kontextes bessere und vollständigere Annotationen vorgenommen werden. Die Verwendung von Genom-Alignments ermĂśglicht eine beliebige Skalierung der vorgeschlagenen Methodik auf Tausende Genome. In dieser Arbeit stelle ich TOGA (Tool to infer Orthologs from Genome Alignments) vor, eine bioinformatische Methode, welche dieses Konzept implementiert und Homologie- Klassifizierung und Gen-Annotation in einer einzelnen Pipeline kombiniert. TOGA verwendet Machine-Learning, um Orthologe von Paralogen basierend auf dem Alignment von intronischer und intergener Regionen zu unterscheiden.
Die Ergebnisse des Benchmarkings zeigen, dass TOGA die herkĂśmmlichen Ansätze innerhalb der Placentalia Ăźbertrifft. TOGA klassifiziert Homologieverhältnisse mit hoher Präzision und identifiziert zuverlässig inaktivierte Gene als solchet. FrĂźhere Versionen von TOGA fanden in mehreren Studien Anwendung und wurden in zwei Publikationen verwendet. AuĂerdem wurde TOGA erfolgreich zur Annotation von 500 Säugetiergeenomen verwendet, dies ist der bisher umfangreichste solche Datensatz. Diese Ergebnisse zeigen, dass TOGA das Potenzial hat, sich zu einer etablierten Methode zur Gen-Annotation zu entwickeln und die derzeit angewandten Techniken zu ergänzen
Computation in Complex Networks
Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue âComputation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: ⢠Community detection ⢠Complex network modelling ⢠Complex network analysis ⢠Node classification ⢠Information spreading and control ⢠Network robustness ⢠Social networks ⢠Network medicin
Selected Works in Bioinformatics
This book consists of nine chapters covering a variety of bioinformatics subjects, ranging from database resources for protein allergens, unravelling genetic determinants of complex disorders, characterization and prediction of regulatory motifs, computational methods for identifying the best classifiers and key disease genes in large-scale transcriptomic and proteomic experiments, functional characterization of inherently unfolded proteins/regions, protein interaction networks and flexible protein-protein docking. The computational algorithms are in general presented in a way that is accessible to advanced undergraduate students, graduate students and researchers in molecular biology and genetics. The book should also serve as stepping stones for mathematicians, biostatisticians, and computational scientists to cross their academic boundaries into the dynamic and ever-expanding field of bioinformatics
Topology of molecular interaction networks
Abstract Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network 1 topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further
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