Permits to represent protein sequence features. SSE-ACC uses amino acid composition (AAC), k-mer composition, and amino acid composition methods to extract concerned features. It is able to generate 100-dimensional feature vectors. The first 60 dimensions are used to describe the frequency of each amino acid in each of the three possible secondary structure elements and the last 40 dimensions represent the frequency of each amino acid having each of the two possible solvent accessibility states.
Department of Computer Science and Engineering, Information Engineering College, Shanghai Maritime University, Shanghai, China; Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, School of Life Sciences, Fudan University, Shanghai, China; Department of Plant Pathology and Microbiology, University of California, Riverside, CA, USA; Department of Computer Science and Engineering, University of California, Riverside, CA, USA; Institute for Integrative Genome Biology, University of California, Riverside, CA, USA; College of Information Science and Technology, Tsinghua University, Beijing, China
SSE-ACC funding source(s)
Supported by the start-up fund of Shanghai Maritime University; by UCR start-up funds and UC AES-CE RSAP grant; and by NSF grant IIS-0711129.