Compressed Learning and Its Applications to Subcellular Localization
Abstract:One of the main challenges faced by biological applications is to predict protein subcellular localization in automatic fashion accurately. To achieve this in these applications, a wide variety of machine learning methods have been proposed in recent years. Most of them focus on finding the optimal classification scheme and less of them take the simplifying the complexity of biological systems into account. Traditionally, such bio-data are analyzed by first performing a feature selection before classification. Motivated by CS (Compressed Sensing) theory, we propose the methodology which performs compressed learning with a sparseness criterion such that feature selection and dimension reduction are merged into one analysis. The proposed methodology decreases the complexity of biological system, while increases protein subcellular localization accuracy. Experimental results are quite encouraging, indicating that the aforementioned sparse methods are quite promising in dealing with complicated biological problems, such as predicting the subcellular localization of Gram-negative bacterial proteins.
Keywords: Benchmark dataset; Compressed Learning; Gram-negative proteins; K-Nearest Neighbor; Periplasm; Principal Component Analysis; Subcellular localization; Unified Compressed Learning; complexity measure factor; compressive sensing; dimensionality reduction; dipeptide components; encoding scheme; fuzzy K-NN; low-dimensional vectors; multiple-location proteins; positionspecific score matrix; pseudo amino acid; subspace learning
Document Type: Research Article
Publication date: September 1, 2011
- Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.