High Level Of Prediction Accuracy Research Articles

Protein secondary structure prediction using local alignments

The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in α-helical, β-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server.

Market timing and unit trusts: Can you beat the market?

The article reports the results of an investigation into the level of predictive accuracy required to benefit from a market-timing strategy using unit trusts as the investment medium. Three unit trusts within the same management company were used as the assets between which a market timer could switch his or her investment. Switching would depend on the timer's forecast of which of the three investments would produce the best returns in the forthcoming period. Remaining within the family of trusts managed by a single company kept the transaction costs to a minimum. Investments could be made in a general equity, a resources or an income unit trust. As attractive as the potential returns from market timing within a family of unit trusts might appear to be, the levels of predictive accuracy required to beat a buy-and-hold strategy with certainty were found to be extremely high (of the order of 80%). In addition, much of the benefit from timing depends on being in the highest yielding asset for a small, but specific number of periods. Therefore not only does one require a high level of predictive accuracy, but it is important to be correct in the key periods when most of the return above the buy-and-hold is earned.

Near Infrared Reflectance Analysis with Single and Multiproduct Calibrations

Single‐product calibrations for near infrared (NIR) spectrophotometers are normally developed for each type of material to achieve a high level of prediction accuracy. Developing multiproduct calibrations with thousands of samples was not practical with personal computers until recently. Advancements in software now make it possible to include thousands of samples into multiproduct calibrations. This study was conducted to evaluate the accuracy of a multiproduct calibration consisting of forage samples. Large single‐product files of hay and fresh forage, haylage, and small grain silage spectra were available for this study. Every sixth sample from each file was reserved to test calibration accuracy. Single‐product calibrations were developed for each product and a multiproduct calibration was developed for all four products. There was very little difference between the accuracy of the multiproduct calibration containing all samples and the single‐product calibrations. In a second study, a multiproduct calibration developed from 756 haylage and small grain silage samples was used to predict the quality of 100 hay samples. The prediction error was found to be unsatisfactory, so 50, 100, 166, and 500 hay samples were added to the multiproduct calibration file to see if the hay prediction errors could be improved. The expanded multiproduct calibration was more accurate than a custom single‐product hay calibration when <150 hay samples were available to develop a custom calibration or expand the multiproduct calibration. This suggests that if only a few samples are available, it may be more cost‐effective to expand a similar product library and recalibrate than to gather a large number of samples for a new calibration.

Comparison of rotavirus immunoglobulin A coproconversion with other indices of rotavirus infection in a longitudinal study in childhood

In order to determine the sensitivity and reliability of antirotaviral fecal immunoglobulin A (IgA) as an indicator of rotavirus reinfection, the antibody responses to rotavirus of 44 infants with severe rotavirus gastroenteritis recruited on admission to a hospital were studied. Feces were collected daily during hospitalization and weekly thereafter, and sera were obtained every 4 months, for 6 to 32 months (median, 17 months). Antirotaviral IgG, IgA, and IgM were measured by enzyme immunoassay in all samples. Rotavirus antigen, rotavirus-neutralizing antibody, and total IgA were measured in feces. The results showed that use of an IgA index (ratio of specific IgA to total IgA) was unnecessary to identify copro-IgA conversion to rotavirus. The other markers of rotavirus infection tested showed a high level of predictive accuracy of coproconversion in rotavirus-neutralizing antibody. Copro-IgM, serum IgM, and virus in feces were insensitive measures of neutralizing antibody coproconversion. Seroconversion in IgG or IgA was detected in 46% of neutralizing coproconversions. The most sensitive marker, present in 92% of neutralizing coproconversions, was antirotaviral fecal IgA conversion. This correlation of fecal IgA with fecal neutralizing antibody suggests that coproconversions in IgA represent true elevations in antirotaviral IgA with neutralizing capacity. A coproconversion in IgA appears to indicate genuine rotavirus infection. Copro-IgA conversions in feces collected weekly are likely to be more sensitive markers of rotavirus reinfection than are seroconversion and virus detection combined in epidemiological studies of acute diarrhea in children and in rotavirus vaccine trials.

Assessing The Discriminant Validity Of Regression Models And Subjectively Weighted Models Of Judgments

When either regression models or subjectively weighted models are used as aids in making placement decisions, the discriminant validity of these models is of interest. When all predictor information is used in all decisions, models which assign equal weights cannot simultaneously show high levels of predictive accuracy and discriminant validity; in some settings, both regression models and subjectively weighted models may. The discriminant validity of regression models and of subjectively weighted models was investigated in two judgment experiments. Both types of models showed high levels of accuracy and cross-validity in both experiments. Regression models showed discriminant validity in both experiments, while subjectively weighted models failed to show discriminant validity in the second. The homogeneity of cue validities appeared to moderate both the level of discriminant validity and the relationship between similarity of subjective models, across tasks, and discriminant validity.

Validity of the ACT Assessment as an Indicator of Reading Ability

The purpose of this research was to explore the validity of ACT Assessment test results as indi cators of reading skill. This was considered impor tant because of the large and growing number of postsecondary institutions using open admission policies and admitting students with a wide range of reading ability. If ACT scores were found to be predictive of reading performance, ACT data could be used to identify those students who might profit from remedial reading training. Various test score combinations were used to predict students' per formance on numerous reading tests at a large number of postsecondary institutions. These studies indicated a high level of predictive accuracy.

Attitude Similarity and Attraction: The Predictability of a Stranger's Attitudes

Summary Attribution theory raises the possibility that findings of a direct linear relationship between attitude similarity and interpersonal attraction are mediated by the predictability of the other's attitudes and behaviors. In a nonexperimental study, 288 subjects predicted the attitudes of a stranger and compared his attitudes to their own. Findings showed that attitude similarity was directly related to attraction only when subjects were unable to predict the stranger's attitudes. At high levels of predictive accuracy, however, attitude similarity was inversely related to attraction. It is suggested that theoretical statements which link attitude similarity to interpersonal attraction have failed to specify necessary mediating conditions, and that exchange of attitudes and other sentiments permits individuals partially to predict and control social interaction.