Biomedical subjects

Konstantinos Blekas

Publications and source records attributed to Konstantinos Blekas.

3 recordsLinked to original sources

PubMed2006

Magnetization transfer ratio and volumetric analysis of the brain in macrocephalic patients with neurofibromatosis type 1.

The purpose of the study was to evaluate brain myelination by measuring the magnetization transfer ratio (MTR) and to measure grey (GMV) and white matter volume (WMV) in macrocephalic children with neurofibromatosis type 1 (NF1). Seven NF1 patients (aged 0.65-16.67 years) and seven age- and gender-matched controls were studied. A three-dimensional (3D) gradient echo sequence with and without magnetization transfer (MT) prepulse was used for MTR assessment. Volume measurements of GM and WM were performed by applying segmentation techniques on T2-weighted turbo spin echo images (T2WI). MTR of unidentified bright objects (UBOs) on T2WI in cerebellar white matter (52.8+/-3.3), cerebral peduncles (48.5+/-1.5), hippocampus (52.6+/-1.1), internal capsule (55.7+/-0.3), globus pallidus (52.7+/-3.9), and periventricular white matter (52.6+/-1.2) was lower than in the corresponding areas of controls (64.6+/-2.5, 60.8+/-1.3, 56.4+/-0.9, 64.7+/-1.9, 59.2+/-2.3, 63.6+/-1.7, respectively; p<0.05). MTR of normal-appearing brain tissue in patients was not significantly different than in controls. Surface area (mm(2)) of the corpus callosum (809.1+/-62.8), GMV (cm(3)) (850.7+/-42.9), and white matter volume (WMV) (cm(3)) (785.1+/-85.2) were greater in patients than in controls (652.5+/-52.6 mm(2), 611.2+/-92.1 cm(3), 622.5+/-108.7 cm(3), respectively; p<0.05). To conclude, macrocephaly in NF1 patients is related to increased GMV and WMV and corpus callosum enlargement. MTR of UBOs is lower than that of normal brain tissue.

Adolescent↗

PubMed2005

Motif-based protein sequence classification using neural networks.

We present a system for multi-class protein classification based on neural networks. The basic issue concerning the construction of neural network systems for protein classification is the sequence encoding scheme that must be used in order to feed the neural network. To deal with this problem we propose a method that maps a protein sequence into a numerical feature space using the matching scores of the sequence to groups of conserved patterns (called motifs) into protein families. We consider two alternative ways for identifying the motifs to be used for feature generation and provide a comparative evaluation of the two schemes. We also evaluate the impact of the incorporation of background features (2-grams) on the performance of the neural system. Experimental results on real datasets indicate that the proposed method is highly efficient and is superior to other well-known methods for protein classification.

Algorithms↗

PubMed2003

Greedy mixture learning for multiple motif discovery in biological sequences.

MOTIVATION: This paper studies the problem of discovering subsequences, known as motifs, that are common to a given collection of related biosequences, by proposing a greedy algorithm for learning a mixture of motifs model through likelihood maximization. The approach adds sequentially a new motif to a mixture model by performing a combined scheme of global and local search for appropriately initializing its parameters. In addition, a hierarchical partitioning scheme based on kd-trees is presented for partitioning the input dataset in order to speed-up the global searching procedure. The proposed method compares favorably over the well-known MEME approach and treats successfully several drawbacks of MEME. RESULTS: Experimental results indicate that the algorithm is advantageous in identifying larger groups of motifs characteristic of biological families with significant conservation. In addition, it offers better diagnostic capabilities by building more powerful statistical motif-models with improved classification accuracy.

Algorithms↗