![]() Supports parity or error checking and correction (ECC) 1 Gbyte of RAM space, 16 Mbytes of ROM space Alternately supports an external L2 cache controller or integrated L2 cache module Supports pipelined burst, synchronous burst, or asynchronous SRAMs Supports partial update with external byte decode for write enables Supports cache sizes of 256 Kbytes, 512 Kbytes, and 1 Mbyte Configurable for write-through or write-back operation Decoupled address and data buses for pipelining of 60x accesses Supports various operating frequencies and bus divider ratios Note that this is a conceptual block diagram intended to show the basic features rather than how these features are physically implemented on the device.įeaturesThis section summarizes the major features of the 106, as follows: This section provides a block diagram showing the major functional units of the 106 and describes briefly how those units interact.įigure 1 shows the major functional units within the 106. OverviewThe MPC106 provides an integrated high-bandwidth, high-performance, TTL-compatible interface between a 60x processor, a secondary (L2) cache or additional (up to four total) 60x processors, the PCI bus, and main memory. The web server for the proposed MemHyb-SVM is accessible at. Thus, our proposed approach is an effective tool for the discrimination of membrane protein types and might be helpful to researchers/academicians working in the field of Drug Discovery, Cell Biology, and Bioinformatics. The highest success rate of 91.1% and 93.4% on two datasets is obtained by RBF- SVM using leave-one-out cross-validation. Linear, polynomial, and RBF based- SVM with Bose, Chaudhuri, Hocquenghem coding are trained and tested. We employ a powerful sequence encoding scheme by fusing position specific scoring matrix and split amino acid composition to effectively discriminate membrane protein types. We consider both evolutionary and physicochemical features and provide them to our classification system based on support vector machine ( SVM) with error correction code. In this paper, we tackle the interesting problem of classifying membrane protein types using their sequence information. Therefore, an automated method is exceedingly advantageous, which can help in identifying the new membrane protein types based on their primary sequence. Knowledge of membrane protein's structure and function has great importance in biological and pharmacological research. About 50% of available drugs are targeted against membrane proteins. ![]()
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