"Computational biology refers to the use of data analysis, mathematical modeling and computational simulations to understand biological systems and relationships."
The study of the use of computer science in understanding biological systems.
Biology Fundamentals: Understanding the basic concepts of biology such as genetics, molecular biology, biochemistry, and cellular biology is crucial for computational biology.
Statistics and Probability: Statistics and probability are essential for analyzing and interpreting data. A strong understanding of probability distributions, statistical inference, and hypothesis testing is crucial.
Algorithms and Data Structures: Learning programming concepts such as algorithms and data structures is important for developing efficient and effective computational models.
Programming Languages: Knowing programming languages such as Python, R, Perl, Java, C++, and SQL is essential for constructing computational models and analyzing data.
Machine Learning: Machine learning is a field of study that involves developing algorithms and statistical models that enable computers to learn and improve from data without being explicitly programmed.
Network Analysis: Network analysis involves modeling and analyzing the interactions between biological molecules, cells, and systems. Topics include graph theory, topology, and network visualization.
Genomics: Genomics is the study of genes and their function. It involves the analysis of DNA sequences, and topics include sequence alignment, genome assembly, and comparative genomics.
Proteomics: Proteomics is the study of proteins and their function. Topics include protein structure prediction, protein-protein interactions, and protein functional annotation.
Metabolomics: Metabolomics is the study of small molecules produced by cells and tissues. Topics include metabolic pathway analysis and metabolite quantification.
Systems Biology: Systems biology is an interdisciplinary field that involves the study of biological systems as a whole, rather than individual parts. Topics include modeling and simulation of biological systems and metabolic networks.
"An intersection of computer science, biology, and big data, the field also has foundations in applied mathematics, chemistry, and genetics."
"It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers."
"The use of data analysis, mathematical modeling, and computational simulations."
"To understand biological systems and relationships."
"An intersection of computer science, biology, and big data."
"Foundations in applied mathematics, chemistry, and genetics."
"Biological systems and relationships."
"The use of data analysis, mathematical modeling, and computational simulations."
"To build computers."
"An intersection of computer science, biology, and big data."
"Biological systems and relationships."
"Applied mathematics, chemistry, and genetics."
"Mathematical modeling and computational simulations."
"To understand biological systems and relationships."
"An intersection of computer science, biology, and big data."
"It differs from biological computing, a subfield of computer engineering which uses bioengineering to build computers."
"The use of data analysis, mathematical modeling, and computational simulations."
"To understand biological systems and relationships."
"Applied mathematics, chemistry, and genetics."