Bioinformatics BI_V0001

title : Bioinformatics as Viewed by a Computer Scientist

author: Raymond Wan

year: 2011

place of pulbish : University of Tokyo

abstract :

Scientists have been interested in biology (including
genetics and molecular biology) for many centuries. Both
to find out more about plants and animals, but of course
to also learn about human health.
Along with physics and chemistry, biology is one of the
natural sciences that many of us (probably) studied in
school.
Over the last decade or two, the amount and type of data
being generated has required computational methods for
data analysis. Simply put, this is the field of bioinformatics
or computational biology.

Bioinformatics BI_E0010

title : SHARE: A Semantic Web Query Engine for Bioinformatics

author: Ben P. Vandervalk, E. Luke McCarthy, Mark D. Wilkinson

year: 2009

place of pulbish : Springer Berlin Heidelberg

abstract :

Driven by the goal of automating data analyses in the field of bioinformatics, SHARE (Semantic Health and Research Environment) is a specialized SPARQL engine that resolves queries against Web Services and SPARQL endpoints. Developed in conjunction with SHARE, SADI (Semantic Automated Discovery and Integration) is a standard for native-RDF services that facilitates the automated assembly of services into workflows, thereby eliminating the need for ad hoc scripting in the construction of a bioinformatics analysis pipeline.

Bioinformatics BI_E0009

title : European Molecular Biology Organization Practical Course on COMPUTATIONAL MOLECULAR EVOLUTION

author: Giorgos Kotoulas, Antonis Magoulas, Stelios Kastrinakis, Eftichia Mironaki, Pelagia Petraki

year: 2006

place of publish : Germany


abstract :

Bioinformatics BI_E0008

title : A grid-oriented genetic algorithm framework for bioinformatics

author: Hiroaki Imade, Ryohei Morishita, Isao Ono, Norihiko Ono, Masahiro Okamoto

year: 2004

place of pulbish : Springer-Verlag

abstract :

In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks, which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed in our laboratory.

Bioinformatics BI_E0007

title : Bioinformatics Visualization and Integration with Open Standards: The Bluejay Genomic Browser

author: Andrei L. Turinsky1, Andrew C. Ah-Seng1, Paul M.K. Gordon1, Julie N. Stromer1, Morgan L. Taschuk1, Emily W. Xu1, Christoph W. Sensen1

year: 2005

place of pulbish : canada

abstract :

We have created a new Java™-based integrated computational environment for the exploration of genomic data, called Bluejay. The system is capable of using almost any XML file related to genomic data. Non-XML data sources can be accessed via a proxy server. Bluejay has several features, which are new to Bioinformatics, including an unlimited semantic zoom capability, coupled with Scalable Vector Graphics (SVG) outputs; an implementation of the XLink standard, which features access to MAGPIE Genecards as well as any BioMOBY service accessible over the Internet; and the integration of gene chip analysis tools with the functional assignments. The system can be used as a signed web applet, Web Start, and a local stand-alone application, with or without connection to the Internet. It is available free of charge and as open source via http://bluejay.ucalgary.ca.

Bioinformatics BI_E0006

title : Bioinformatics approaches for the classification of G-protein-coupled receptors

author: Anna Gaulton and Teresa K Attwood

year: 2003

place of pulbish : School of Biological Sciences and Department of Computer Science,
University of Manchester, Oxford Road, Manchester M13 9PT, UK

abstract :

G-protein-coupled receptors are found abundantly in the human
genome, and are the targets of numerous prescribed drugs.
However, many receptors remain orphaned (i.e. with unknown
ligand specificity), and others remain poorly characterised, with
little structural information available. Consequently, there is often
a gulf between sequence data and structural and functional
knowledge of a receptor. Bioinformatics approaches may offer
one approach to bridging this gap. In particular, protein family
databases, which distil information from multiple sequence
alignments into characteristic signatures, could be used to
identify the families to which orphan receptors belong, and might
facilitate discovery of novel motifs associated with ligand binding
and G-protein-coupling.

Bioinformatics BI_E0005

title : Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics

author: Marylyn D. Ritchie, Christopher S. Coffey, Jason H. Moore

year: 2004

place of pulbish : Springer Berlin Heidelberg

abstract :

The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.