Review of algorithms for RNA secondary structure prediction with pseudoknots

Pseudoknots are structures that are formed from the base pairing of an RNA secondary loop structure with a complementary base which lies somewhere outside of the loop. The result is a structure, which plays a vital role in cell structure rigidity, regulation of protein synthesis, and in the structural organization of RNA complexes. Deciphering RNA folding patterns would begin to unravel some of the mysteries surrounding the cell and its functions and open a new world to scientists. Many algorithms have been written in this quest to predict RNA\u27s secondary structure but not many have been very successful. In this thesis, some of these algorithms are discussed and considered for their strengths and weaknesses. First those algorithms, which exclude pseudoknots and other more complex structures, are presented. The later algorithms include those, which attempt to include some of the more complex structures into their calculations. In the end, all the algorithms are taken into consideration and their strengths and weaknesses compared so as to find some path for future direction. By using the strengths found in these variety of algorithms and avoiding some of the piffalls encountered by others hopefully new algorithms will be developed in the future that are more successful in deciphering RNA secondary structure

A Customer Segmentation Mining System on the Web Platform

We will introduce a knowledge discovery system developed on the World Wide Web platform in this paper. Its algorithm is based on Fuzzy Inductive Learning Method (FILM), which can segment consumers\u27 behavior from a set of customer data with noises. In a visualization way, the system will present the acquired knowledge as a set of IF-THEN rules that can be run on top of an expert system. Moreover, the system will provide advices in response to a user\u27s request through the network and a friendly user interface. At last, we evaluate the function of the system by training it with a transaction database provided by a local automobile dealer

Automated nasal feature detection for the lexical access from features project

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 150-151).The focus of this thesis was the design, implementation, and evaluation of a set of automated algorithms to detect nasal consonants from the speech waveform in a distinctive feature-based speech recognition system. The study used a VCV database of over 450 utterances recorded from three speakers, two male and one female. The first stage of processing for each speech waveform included automated 'pivot' estimation using the Consonant Landmark Detector - these 'pivots' were considered possible sonorant closures and releases in further analyses. Estimated pivots were analyzed acoustically for the nasal murmur and vowel-nasal boundary characteristics. For nasal murmur, the analyzed cues included observing the presence of a low frequency resonance in the short-time spectra, stability in the signal energy, and characteristic spectral tilt. The acoustic cues for the nasal boundary measured the change in the energy of the first harmonic and the net energy change of the 0-350Hz and 350-1000Hz frequency bands around the pivot time. The results of the acoustic analyses were translated into a simple set of general acoustic criteria that detected 98% of true nasal pivots. The high detection rate was partially offset by a relatively large number of false positives - 16% of all non-nasal pivots were also detected as showing characteristics of the nasal murmur and nasal boundary. The advantage of the presented algorithms is in their consistency and accuracy across users and contexts, and unlimited applicability to spontaneous speech.by Neira Hajro.M.Eng

Modelling and Implementing Macro Web Navigation Structures

From the three existing dimensions - content objects, navigation and presentation - of web pages, the navigation dimension is analysed. It consists of macro, meso and micro navigation. A conceptual approach to model and implement the macro navigation structure of web sites is presented. We start by enhancing a function decomposition diagram to model the macro navigation structure. The resulting graph is analysed and transformed to find its crucial components. In order to store these components in a database table, an appropriate entity-relationship model is developed. Afterwards we identify three navigation styles that can be produced from the underlying database and give the PHP source code for one of them. Finally, an example web page using our navigation styles is depicted. It is concluded that using our ideas may lead to reduced development time and avoids link errors in the process of creating the site navigation

The Geometric Mean as a Generator of Truth-Value in Heuristic Expert Systems: An Improvement over the Fuzzy Weighted Arithmetic Mean

Many earlier expert systems that were modeled after MYCIN, the first expert system, employed truth-value factors for their rule antecedents (premises) and consequents (conclusions). These crisp truth-value factors were usually called certainty factors and attempted to provide a measure of confidence and computational capability to the analysis of rule uncertainty (Shortliffe, 1977; Kandel, 1994). However, in the literature criticism has been often expressed concerning the lack of precision a crisp truth/certainty factor value conveys (Zadeh, 1983; Turban, 1993). Zadeh (1973) and Xingui (1988) utilized the weighted fuzzy average algorithm to improve the precision of truth/certainty factor values. Kandel (1994) further extended the fuzzy weighted mean concept introducing rule confidence, priority, and conclusion weighting factors. Later, Chen (1996) further modified the fuzzy weighted mean algorithm through the factoring of independent rule premise and consequent weights, truth-values and certainty factors. All of these progressive variants of the fuzzy weighted mean enhanced perceived rule antecedent and consequent truth-value. This research investigated a modification of the fuzzy weighted algorithms of Chen and Kandel utilized in assessing heuristic expert system rule truth-value. Their algorithms were modified to demonstrate that a more statistically precise rule truth-value can be achieved by utilizing the geometric mean to aggregate rule truth-value components

Additive Pattern Database Heuristics

We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem