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Biomedical subjects

D di Bernardo

Publications and source records attributed to D di Bernardo.

6 recordsLinked to original sources
PubMed2006

A multi-step approach to time series analysis and gene expression clustering.

MOTIVATION: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation. RESULTS: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters. AVAILABILITY: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Artificial Intelligence↗
PubMed2005

The transcriptional landscape of the mammalian genome.

This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

3' Untranslated Regions↗
PubMed2000

Dispersion of QT intervals: a measure of dispersion of repolarization or simply a projection effect?

QT interval dispersion may provide little information about repolarization dispersion. Some clinical measurements demonstrate an association between high QT interval dispersion and high morbidity and mortality, but what is being measured is not clear. This study was designed to help resolve this dilemma. We compared the association between different clinical measures of QT interval dispersion and the ECG lead amplitudes derived from a heart vector model of repolarization with no repolarization dispersion whatsoever. We compared our clinical QT interval dispersion data obtained from 25 subjects without cardiac disease with similar data from published studies, and correlated these QT dispersion results with the distribution of lead amplitudes derived from the projection of the heart vector onto the body surface during repolarization. Published results were available for mean relative QT intervals and mean differences from the maximum QT interval. The leads were derived from Uijen and Dower lead vector data. Using the Uijen lead vector data, the correlation between measurements of dispersion and derived lead amplitudes ranged from 0.78 to 0.99 for limb leads, and using the Dower values ranged from 0.81 to 0.94 for the precordial leads. These results show a clear association between the measured QT interval dispersion and the variation in ECG lead amplitudes derived from a simple heart vector model of repolarization with no regional information. Therefore, measured QT dispersion is related mostly to a projection effect and is not a true measure of repolarization dispersion. Our existing interpretation of QT dispersion must be reexamined, and other measurements that provide true repolarization dispersion data investigated.

Adolescent↗
PubMed2000

Computer model for study of cardiac repolarization.

INTRODUCTION: We propose a new and simple method to model repolarization in the left ventricle and the corresponding T waves on the surface ECG. METHODS AND RESULTS: We modeled the cardiac cell action potentials (APs) in the left ventricle (LV) with differences in only the duration of the plateau phase. Using published experimental data on the epicardial and endocardial repolarization sequences, for each point on the left ventricular surface we set a different AP repolarization starting time, determined by the duration of the plateau phase. The surface source model was used to compute potentials on the surface of the torso, generated by repolarization of the LV. Both the torso and the LV had homogeneous and isotropic conductivity. We simulated T waves on the 12-lead ECG and compared our results with measured T waves from five normal subjects. The orientation and shape in each lead were reproduced. In each lead we computed the root mean square error between simulated and measured T waves. The average error across the 12 leads was small, with a mean value of 0.11 mV across all the subjects. CONCLUSION: Repolarization of the LV can be modeled independently of the depolarization sequence and AP duration gradients. This method is an easy and powerful tool to describe the ECG features of repolarization.

Action Potentials↗