24,809 research outputs found
"DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR BIOINFORMATICS. EXTRACTION OF PROTEIN COMPLEXES FROM A PROTEIN-PROTEIN INTERACTION NETWORK: A CASE STUDY"
Decision Support Systems and Workflow Management Systems have
become essential tools for some business and scientific field. This
thesis propose a new hybrid architecture for problem solving expertise
and decision-making process, that aims to support high-quality
research in the field of bioinformatics and system biology.
The first part of the dissertation introduces the project to which belong
this thesis work, i.e. the “Bioinformatics Organized Resources -
an Intelligent System” (BORIS) project of the ICAR-CNR; the main
goal of BORIS is to provide an helpful and effective support to researchers
or experimentalist, that have no familiarity with tools and
techniques to solve computational problems in bioinformatics and system
biology.
In the second part of the thesis, the proposed hybrid architecture is
described in detail; it introduces a three-dimensional space for the
BORIS system, where the viewpoints of declarative, procedural and
process approaches are considered. Using the proposed architecture,
the system is able to help the experimentalist choosing, for a given
problem, the right tool at the right moment, to generate a navigable
Workflow at different abstraction layers, extending current workflow
management systems and to free the user from implementation details,
assisting him in the correct configuration of algorithms/services.
A case study about extraction of protein complexes from proteinprotein
interaction networks is presented, in order to show how the
system faces a problem and how it interacts with the user
Mean-Field Theory of Meta-Learning
We discuss here the mean-field theory for a cellular automata model of
meta-learning. The meta-learning is the process of combining outcomes of
individual learning procedures in order to determine the final decision with
higher accuracy than any single learning method. Our method is constructed from
an ensemble of interacting, learning agents, that acquire and process incoming
information using various types, or different versions of machine learning
algorithms. The abstract learning space, where all agents are located, is
constructed here using a fully connected model that couples all agents with
random strength values. The cellular automata network simulates the higher
level integration of information acquired from the independent learning trials.
The final classification of incoming input data is therefore defined as the
stationary state of the meta-learning system using simple majority rule, yet
the minority clusters that share opposite classification outcome can be
observed in the system. Therefore, the probability of selecting proper class
for a given input data, can be estimated even without the prior knowledge of
its affiliation. The fuzzy logic can be easily introduced into the system, even
if learning agents are build from simple binary classification machine learning
algorithms by calculating the percentage of agreeing agents.Comment: 23 page
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics
In contemporary age, Computational Intelligence (CI) performs an essential
role in the interpretation of big biological data considering that it could
provide all of the molecular biology and DNA sequencing computations. For this
purpose, many researchers have attempted to implement different tools in this
field and have competed aggressively. Hence, determining the best of them among
the enormous number of available tools is not an easy task, selecting the one
which accomplishes big data in the concise time and with no error can
significantly improve the scientist's contribution in the bioinformatics field.
This study uses different analysis and methods such as Fuzzy, Dempster-Shafer,
Murphy and Entropy Shannon to provide the most significant and reliable
evaluation of IoT-based computational intelligence tools for DNA sequence
analysis. The outcomes of this study can be advantageous to the bioinformatics
community, researchers and experts in big biological data
Recommended from our members
An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
- …