Biomedical subjects

Luca Benini

Publications and source records attributed to Luca Benini.

4 recordsLinked to original sources

PubMed2006

TOM: a web-based integrated approach for identification of candidate disease genes.

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals. The algorithm uses the information stored in public resources, including mapping, expression and functional databases. Given the queries, TOM will select and list one or more candidate genes. This approach allows the geneticist to bypass the costly and time consuming tracing of genetic markers through entire families and might improve the chance of identifying disease genes, particularly for rare diseases. We present here the tool and the results obtained on known benchmark and on hereditary predisposition to familial thyroid cancer. Our algorithm is available at http://www-micrel.deis.unibo.it/~tom/.

Algorithms↗

PubMed2005

Automatic intrinsic DNA curvature computation from AFM images.

Critical information on several biological processes such as DNA-protein interactions and DNA transcription can be derived from analysis of DNA curvature. Under thermal perturbation, the curvature is composed of static and dynamic contributions, thus, can be described as the sum of intrinsic curvature and a fluctuation contribution. Without considering thermal agitations, the DNA curvature is reducible to the intrinsic component, which is a function of the DNA nucleotide sequence only. In this paper, we present an automated algorithm to determine the DNA intrinsic curvature profiles and the molecular spatial orientations in Atomic Force Microscope images. The algorithm allows to reconstruct the intrinsic curvature profile by filtering the thermal contribution. It detects fragment orientation on atomic force microscope images without labels with a percentage of correct molecular-orientation detection of 96.79% in computer-generated benchmarks, for molecules with a high curvature peak. The automated algorithm reconstructs the intrinsic curvature profile of DNA molecules with a mean square error of 3.8122 x 10(-4) rads over a profile with a central peak value of 0.196 rads, and 6.1 x 10(-3) rads over a curvature profile with two symmetric peaks of about 0.08 rads. Moreover, it correctly detects the location of the peaks in the molecules with a deviation of about 1% of molecule length.

Algorithms↗

PubMed2005

Automated DNA fragments recognition and sizing through AFM image processing.

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%.

Algorithms↗

PubMedDate not supplied

Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams.

The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence. In this work, we propose a novel biclustering algorithm that exploits the zero-suppressedbinary decision diagrams (ZBDDs) data structure to cope with the computational challenges. Our method can find all biclusters that satisfy specific input conditions, and it is scalable to practical gene expression data. We also present experimental results confirming the effectiveness of our approach.

Algorithms↗