Introduction to Machine Learning
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.
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- Enhancement of Gene Gun-Induced Vaccine-Specific Cytotoxic T-Cell Response by Administration of Chemotherapeutic Drugs
- Measuring Immune Responses to Recombinant AAV Gene Transfer
- Identification and Characterization of Herpesviral Immediate-Early Genes
- Analysis of Comparative Genomic Hybridization Data on cDNA Microarrays
- DNA Extraction from Aged Skeletal Samples for STR Typing by Capillary Electrophoresis
- Simple Methods for the Detection of HLA-G Variants in Coding and Non-coding Regions
- Measurement of Cardiac Gene Expression by Reverse Transcription Polymerase Chain Reaction (RT-PCR)
- Fluorescence In Situ Hybridization for the Detection of Chromosome Aberrations and Aneuploidy Induced by Environmental Toxicants
- Immunohistochemical and Immunofluorescence Procedures for Protein Analysis