Protein secondary structure prediction using BLAST and relaxed threshold rule induction from coverings

Protein structure prediction has always been an important research area in bioinformatics and biochemistry. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q₃ accuracy of various computational prediction methods rarely has exceeded 75%; this status has changed little since 2003 when Rost stated that the currently best methods reach a level around 77% three-state per-residue accuracy. The application of artificial neural network methods to this problem is revolutionary in the sense that those techniques employ the homologues of proteins for training and prediction. In this dissertation, a different approach, RT-RICO (Relaxed Threshold Rule Induction from Coverings), is presented that instead uses association rule mining. This approach still makes use of the fundamental principle that structure is more conserved than sequence. However, rules between each known secondary structure element and its neighboring amino acid residues are established to perform the predictions. This dissertation consists of five research articles that discuss different prediction techniques and detailed rule-generation algorithms. The most recent prediction approach, BLAST-RT-RICO, achieved a Q₃ accuracy score of 89.93% on the standard test dataset RS126 and a Q₃ score of 87.71% on the standard test dataset CB396, an improvement over comparable computational methods. Herein one research article also discusses the results of examining those RT-RICO rules using an existing association rule visualization tool, modified to account for the non-Boolean characterization of protein secondary structure --Abstract, page iv

Mind as a Virtual Phase-Conjugated Hologram

Because of its superior information processing capability, previous authors have proposed that phase conjugation holography offers a feasible mechanism to explain various aspects of human perception. These previous models focused on the relationship between the perceived image of an object and the actual object with little attention to the anatomical location of the phase-conjugation mirror. The present article proposes that phase-conjugation mirrors exist in the brain as 3D networks of organic molecules previously observed to exhibit phase-conjugation behavior. In particular rhodopsin photoreceptor molecules are proposed to form extra-retinal, deep brain networks which function as phase-conjugation mirrors which are distributed throughout the brain. Furthermore, such networks are proposed to convert endogenous biophotons into virtual holograms which function to store cognitive information in the brain. Such a system offers a new functional definition of the mind