Computational Molecular Biology
An interdisciplinary research field for molecular biologists and computer scientists who are desiring to understand the major issues concerning representation and analysis of genomes, sequences and proteins. Algorithms and methods in biological sequence analysis, with a strong emphasis on probabilistic methods, and systems biology. Discovery of gene regulatory networks, quantitative modeling of gene regulatory networks in system biology.
DNA Microarrays
Microarrays are revolutionary techniques for monitoring gene expression changes. DNA microarray consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, each containing picomoles of a specific DNA sequence. DNA microarrays can be used to measure changes in expression levels, to detect SNPs, in genotyping or in resequencing mutant genomes. Microarrays also differ in fabrication, workings, accuracy, efficiency, cost, the experimental design and the methods of analyzing the data. Typical applications: gene expression profiling, SNP detection, Alternative splicing detection, Gene ID (small microarrays to check IDs of organisms in food and feed) and so many other evolving ideas in bioinformatics.
Heuristic Optimization
Optimization problems are concerned with finding the values for one or several decision variables that meet the objective the best without violating the constraint. Heuristic search and optimization is a new approach for solving complex problems that overcomes many shortcomings of traditional optimization techniques. Evolutionary techniques, simulated annealing, some population based methods replicate the combined intelligence of crowds or the collective behaviour of social animals are few from many Heuristic Optimization techniques.Typical applications to:- risk and portfolio management, financial econometrics, power systems, complex biological systems modelling and many other evolving areas ...
Image Processing
Image processing, in computer science is any form of signal processing for which the input is an image, such as photographs or frames of video; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Typical applications: image recognition, image segmentation, face detection, feature detection, medical image processing, DNA microarray analysis, remote sensing and so many other evolving areas ...
Modelling Biological Systems
To develop and use efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of biological systems, like cellular subsystems (such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Bioinformatics and computational biology are directly associated. Typical applications: Ecosystem models, A cellular model, Protien model, Human biological systems and Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.
Machine Learning
A scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. Focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. It is closely related to statistics, probabilty theory, pattern recognition, artificial intelligence and theoritical computer science. Typical applications: machine perception, computer vision, natural language processing, search engines, bioinformatics, classifying DNA sequences, stock market prediction and many other areas ...
Optimization Method
Developments in the theory and realization of optimization methods. Implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization, optimal control and multi-objective and global optimization by deterministic or nondeterministic algorithms. Typical implementations: automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Applications of optimization methods and software in specific areas such as engineering, machine learning, data mining, economics, finance, biology, or medicine.
Pattern Recognition
Pattern recognition aims to classify data (patterns) based either on a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. Typical applications: Pattern recognition is the basis for computer-aided dianosis systems which describes a procedure that supports the doctor's interpretations and findings; automatic speech recognition, image analysis.
