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The field of study that combines biology and computer science to analyze and interpret biological data using computational techniques.
The application of computer science and information technology to the field of biology, particularly the analysis of biological data such as DNA sequences.
The study of an organism's complete set of DNA, including the arrangement of genes and their interactions.
The large-scale study of proteins, including their structures, functions, and interactions within an organism.
The study of an organism's complete set of RNA transcripts, including their abundance and variation under different conditions.
The study of an organism's complete set of small molecules, including metabolites and their interactions within a biological system.
The process of arranging DNA, RNA, or protein sequences to identify regions of similarity, often used to infer evolutionary relationships.
The study of evolutionary relationships among organisms based on similarities and differences in their genetic material.
The study of how genes are turned on or off in different cells or under different conditions, often using techniques such as microarrays or RNA sequencing.
The study of the three-dimensional structures of biological macromolecules, such as proteins and nucleic acids, and their interactions.
The study of biological systems as a whole, including the interactions and dynamics of their components, often using computational models.
The process of discovering patterns and relationships in large biological datasets, often using computational algorithms.
The use of computational algorithms and statistical models to enable computers to learn from and make predictions or decisions based on biological data.
The application of statistical methods to analyze and interpret biological data, often used to test hypotheses and make inferences.
The study of biological networks, such as gene regulatory networks or protein-protein interaction networks, to understand their structure and function.
The study of how species have evolved and diversified over time, often using genetic and genomic data to reconstruct evolutionary relationships.
The use of computational methods to identify and design new drugs or optimize existing drugs, often based on their interactions with biological targets.
The study of the genetic changes and alterations that occur in cancer cells, often using genomic sequencing and analysis techniques.
The study of heritable changes in gene expression or cellular phenotype that do not involve changes to the underlying DNA sequence.
The study of genetic material recovered directly from environmental samples, such as soil or water, to understand the diversity and function of microbial communities.
The use of microarrays, or DNA chips, to measure the expression levels of thousands of genes simultaneously, often used to study gene expression patterns.
High-throughput DNA sequencing technologies that enable the rapid and cost-effective sequencing of large amounts of genetic material.
The use of computational methods to predict the three-dimensional structure of proteins based on their amino acid sequences.
The complex network of interactions between genes and their regulatory elements that control gene expression in a cell or organism.
The study of the similarities and differences in the genomes of different species to understand their evolutionary relationships and functional elements.
The study of the functions and interactions of genes and their products, often using high-throughput experimental techniques and computational analysis.
The study of the biological pathways and networks that underlie cellular processes, often used to understand the functional implications of genomic data.
The study of how an individual's genetic makeup influences their response to drugs, often used to personalize drug treatments.
The study of the physical interactions between proteins, often used to understand protein function and cellular processes.
The use of high-throughput sequencing technologies to study gene expression at the transcriptome level, often used to identify novel transcripts or alternative splicing events.
The identification of short, conserved sequences in DNA or protein sequences that are associated with specific biological functions or regulatory elements.
The use of computational methods to analyze and predict the three-dimensional structures of biological macromolecules, such as proteins and nucleic acids.
The application of systems biology approaches to understand and treat human diseases, often integrating clinical and molecular data.
The study of all the RNA transcripts produced by a cell or organism, often used to understand gene expression patterns and regulatory mechanisms.
The study of the biochemical reactions and pathways that occur within a cell or organism, often used to understand metabolic processes and their regulation.
The use of the Gene Ontology, a controlled vocabulary of terms describing gene function and biological processes, to analyze and interpret genomic data.
The use of computational methods to predict the function of proteins based on their sequence or structure, often used to annotate newly sequenced genomes.
The use of mathematical models to simulate and predict the behavior of metabolic networks, often used to understand cellular metabolism and design metabolic engineering strategies.
The construction and analysis of networks that represent the co-expression patterns of genes across different conditions or tissues, often used to identify functional modules or regulatory relationships.
The process by which a protein adopts its three-dimensional structure, often driven by the interactions between its amino acid residues.
The use of genetic and biochemical techniques to modify metabolic pathways in organisms, often used to produce valuable compounds or improve industrial processes.
Short, conserved DNA sequences that are recognized by transcription factors and play a role in the regulation of gene expression.
The physical interactions between proteins and DNA, often involved in the regulation of gene expression and other cellular processes.
The quantification of the flow of metabolites through metabolic pathways, often used to understand and optimize cellular metabolism.
The measurement of the expression levels of thousands of genes simultaneously, often used to study gene expression patterns and identify biomarkers.
The prediction of the three-dimensional structure of a protein complex formed by the interaction of two or more proteins.
The process of inferring the complete set of metabolic reactions and their interconnections within an organism, often used to model and simulate cellular metabolism.
The study of the effects of inactivating or deleting a specific gene in an organism, often used to understand gene function and biological processes.