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Biomedical subjects

Esa Uusipaikka

Publications and source records attributed to Esa Uusipaikka.

2 recordsLinked to original sources

Parametric methods for estimating covariate-dependent reference limits.

Age-specific reference limits are required for many clinical laboratory measurements. Statistical assessment of calculated intervals must be performed to obtain reliable reference limits. When parametric, covariate-dependent limits are derived, normal distribution theory usually is applied due to its mathematical simplicity and relative ease of fitting. However, it is not always possible to transform data and achieve a normal distribution. Therefore, models other than those based on normal distribution theory are needed. Generalized linear model theory offers one such alternative. Regardless of the statistical model used, the assumptions behind the model should always be examined.

Humans↗

Efficient estimation of emission probabilities in profile hidden Markov models.

MOTIVATION: Profile hidden Markov models provide a sensitive method for performing sequence database search and aligning multiple sequences. One of the drawbacks of the hidden Markov model is that the conserved amino acids are not emphasized, but signal and noise are treated equally. For this reason, the number of estimated emission parameters is often enormous. Focusing the analysis on conserved residues only should increase the accuracy of sequence database search. RESULTS: We address this issue with a new method for efficient emission probability (EEP) estimation, in which amino acids are divided into effective and ineffective residues at each conserved alignment position. A practical study with 20 protein families demonstrated that the EEP method is capable of detecting family members from other proteins with sensitivity of 98% and specificity of 99% on the average, even if the number of free emission parameters was decreased to 15% of the original. In the database search for TIM barrel sequences, EEP recognizes the family members nearly as accurately as HMMER or Blast, but the number of false positive sequences was significantly less than that obtained with the other methods. AVAILABILITY: The algorithms written in C language are available on request from the authors.

Algorithms↗