Screening of obstructive and central apnoea/hypopnoea in children using variability: A preliminary study

Aim: Polysomnography (PSG) is the current standard protocol for sleep disordered breathing (SDB) investigation in children. Presently, there are limited reliable screening tests for both central (CE) and obstructive (OE) respiratory events. This study compared three indices, derived from pulse oximetry and electrocardiogram ( ECG), with the PSG gold standard. These indices were heart rate (HR) variability, arterial blood oxygen de-saturation (SaO(2)) and pulse transit time (PTT). Methods: 15 children (12 male) from routine PSG studies were recruited (aged 3 - 14 years). The characteristics of the three indices were based on known criteria for respiratory events (RPE). Their estimation singly and in combination was evaluated with simultaneous scored PSG recordings. Results: 215 RPE and 215 tidal breathing events were analysed. For OE, the obtained sensitivity was HR (0.703), SaO(2) (0.047), PTT (0.750), considering all three indices (0) and either of the indices (0.828) while specificity was (0.891), (0.938), (0.922), (0.953) and (0.859) respectively. For CE, the sensitivity was HR (0.715), SaO(2) (0.278), PTT (0.662), considering all indices (0.040) and either of the indices (0.868) while specificity was (0.815), (0.954), (0.901), (0.960) and (0.762) accordingly. Conclusions: Preliminary findings herein suggest that the later combination of these non-invasive indices to be a promising screening method of SDB in children.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

Predicting structural disruption of proteins caused by crossover

We present a machine learning model that predicts a structural disruption score from a protein’s primary structure. SCHEMA was introduced by Frances Arnold and colleagues as a method for determining putative recombination sites of a protein on the basis of the full (PDB) description of its structure. The present method provides an alternative to SCHEMA that is able to determine the same score from sequence data only. Circumventing the need for resolving the full structure enables the exploration of yet unresolved and even hypothetical sequences for protein design efforts. Deriving the SCHEMA score from a primary structure is achieved using a two step approach: first predicting a secondary structure from the sequence and then predicting the SCHEMA score from the predicted secondary structure. The correlation coefficient for the prediction is 0.88 and indicates the feasibility of replacing SCHEMA with little loss of precision. ©2005 IEEE

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Exploiting sequence dependencies in the prediction of peroxisomal proteins

Prediction of peroxisomal matrix proteins generally depends on the presence of one of two distinct motifs at the end of the amino acid sequence. PTS1 peroxisomal proteins have a well conserved tripeptide at the C-terminal end. However, the preceding residues in the sequence arguably play a crucial role in targeting the protein to the peroxisome. Previous work in applying machine learning to the prediction of peroxisomal matrix proteins has failed W capitalize on the full extent of these dependencies. We benchmark a range of machine learning algorithms, and show that a classifier - based on the Support Vector Machine - produces more accurate results when dependencies between the conserved motif and the preceding section are exploited. We publish an updated and rigorously curated data set that results in increased prediction accuracy of most tested models.

Predicting peroxisomal proteins

PTS1 proteins are peroxisomal matrix proteins that have a well conserved targeting motif at the C-terminal end. However, this motif is present in many non peroxisomal proteins as well, thus predicting peroxisomal proteins involves differentiating fake PTS1 signals from actual ones. In this paper we report on the development of an SVM classifier with a separately trained logistic output function. The model uses an input window containing 12 consecutive residues at the C-terminus and the amino acid composition of the full sequence. The final model gives a Matthews Correlation Coefficient of 0.77, representing an increase of 54% compared with the well-known PeroxiP predictor. We test the model by applying it to several proteomes of eukaryotes for which there is no evidence of a peroxisome, producing a false positive rate of 0.088%.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.