Random expectation

Transitive, iterative searches initiated with the sequences of the anticodon-binding domains of lysyl- and aspartyl-tRNA synthetases provided leads for the identification of biologically interesting, previously unknown OB-fold domains at a statistically significant level (random expectation values <0.01). In particular, OB folds were detected in the eukaryotic replication factor RFA with statistically significant scores this... 

Now let us examine the distribution and position of disulfides in proteins. The simplest consideration is distribution in the sequence (see Fig. 51), which is apparently quite random, except that there must be at least two residues in between connected half-cystines. Even rather conspicuous patterns such as two consecutive halfcystines in separate disulfides turn out, when the distribution is plotted for the solved structures (Fig. 51), to occur at only about the random expected frequency. The sequence distribution of halfcystines is influenced by the statistics of close contacts in the three-dimensional structures, but apparently there are no strong preferences of the cystines that could influence the three-dimensional structure. 

Erom the RegionMiner analysis it is clear that estrogen receptor elements (EREs) are strongly overrepresented in enriched regions. ER is found at 2116 sequences. The random expectation would be only 825 matches. This results in... 

The main objective of this experiment was to demonstrate that a peptide lead compound could be used in rational design of a non-peptide library. One of the natural opiates, met-enkephalin, is used as a hypothetical lead compound. The averaged frequency distribution based on four SA runs is obtained (data not shown). Based on this result, 03 had the highest frequency, and the frequencies of A4, Dll, D13, D14, D16, D2, D3, D5, and D9 are also above random expectation. Apparently, 03 appeared in all the reported active peptoids with opioid activity (cf. Table 1). Comparison of the structure of met-enkephalin (Fig. 5) with 03 indicated that 03 is similar to the side chain of tyrosine, which is the N-terminal residue of met-enkephalin. Among other building blocks found more frequently than random expectation, A4, D3, and D13 are present in the reported opioid peptoids (cf. Table 1). Thus, the SA sampling correctly identified four... 

This experiment represents a scenario when an organic lead compound is available. We chose morphine, a known opiate receptor ligand of non-peptide chemical nature, as a hypothetical lead compound. The averaged frequency distribution based on all four SA runs is obtained (data not shown). The most frequent building block was Dll. Building blocks DIO, D12, D14, and 03 were less frequent, but all above random expectation. 

Figure 1. The percent o f salamandeis tested that avoided (had fewer recorded locations on) the TSpc side of the test dish during trials from November 1999 to April 2000. The number of salamanders tested on each date is indicated above each circle. Unlabelled circles represent results from salamanders that had previously been exposed to TSPC, with the number of exposures increasing with time through the winter. Salamanders that had just been captured and were tested for the first time are desigrrated as fresh (F) salamanders. These fresh salamanders were tested with the same test samples at similar times as captive salamanders repeatedly exposed to TSPC for 5 months during summer (S) or winter (W). Significant statistical outcomes (P < 0.05) from random expectation (at 50 %) are indicated by solid circles non-significant results, by open circles.

To further verify the validity of the algorithm and the random expected value model, we simulate different values of parameters of hybrid intelligent algorithm ... 

Building the Model of Fuzzy Random Expected Value... 

Considering the fuzzy stochastic customer demand in the three-level supply chain, we apply the fuzzy expectation theory to build a supply chain logistics planning fuzzy random expected value programming model with the restriction of manufacturing capacity, storage capacity, and transportation capacity. 

The classification results obtained with linear discriminants are strongly affected by the ratio of the training set size, n, and the number of variables per observation, d. This point has been discussed in a number of recent pn-pers. ° The probability of correctly classifying 100% of the members of a training set due to chance is low for n/d > 3, but substantial classification success above the random expectation of 50% can still be obtained. For example, when n/d - 5 the probability is one-half that 77% of the members of the training set will be correctly classified as a result of chance alone. The results reported in recent papers place limits on the types of problems that can be attacked by this type of pattern recognition approach, and they provide measures by which classification results can be judged. 

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