Biomedical subjects

Erkan U Mumcuoğlu

Publications and source records attributed to Erkan U Mumcuoğlu.

4 recordsLinked to original sources

PubMed2006

A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets.

In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides. The method is tested to see its ability to classify proteins into families on a subset of SCOP family database and compared against many of the existing homology detection methods including the most popular generative methods; SAM-98 and PSI-BLAST and the recent SVM methods; SVM-Fisher, SVM-BLAST and SVM-Pairwise. The results have demonstrated that the new method significantly outperforms SVM-Fisher, SVM-BLAST, SAM-98 and PSI-BLAST, while achieving a comparable accuracy with SVM-Pairwise. In terms of efficiency, it performs much better than SVM-Pairwise. It is shown that the information of n-peptide compositions with reduced amino acid alphabets provides an accurate and efficient means of protein vectorization for SVM-based sequence classification.

Amino Acid Sequence↗

PubMed2006

SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees.

A new method based on probabilistic suffix trees (PSTs) is defined for pairwise comparison of distantly related protein sequences. The new definition is adopted in a discriminative framework for protein classification using pairwise sequence similarity scores in feature encoding. The framework uses support vector machines (SVMs) to separate structurally similar and dissimilar examples. The new discriminative system, which we call as SVM-PST, has been tested for SCOP family classification task, and compared with existing discriminative methods SVM-BLAST and SVM-Pairwise, which use BLAST similarity scores and dynamic-programming-based alignment scores, respectively. Results have shown that SVM-PST is more accurate than SVM-BLAST and competitive with SVM-Pairwise. In terms of computational efficiency, PST-based comparison is much better than dynamic-programming-based alignment. We also compared our results with the original family-based PST approach from which we were inspired. The present method provides a significantly better solution for protein classification in comparison with the family-based PST model.

Algorithms↗

PubMed2006

Simultaneous surface registration of ictal and interictal SPECT and magnetic resonance images for epilepsy studies.

BACKGROUND: Subtraction of ictal and interictal single photon emission computed tomography (SPECT) images is known to be successful in localizing the seizure focus in the pre-surgical evaluation of patients with partial epilepsy. A computer-aided methods for producing subtraction ictal SPECT co-registered to the magnetic resonance image (MRI) (the SISCOM method) is commonly used. The two registrations involved in SISCOM are (1) between the ictal-interictal SPECT images, which was shown to be the more critical, and (2) between the ictal image and MRI. OBJECTIVE: To improve the accuracy of ictal-interictal registration in SISCOM by registering all three images (ictal, interictal SPECT, MRI) simultaneously. METHODS: The registration problem is formulated as the minimization of a cost function between three surfaces. Then, to achieve a global minimum of this cost function, the Powell algorithm with randomly distributed initial configurations is used. This technique is tested by a realistic simulation study, a phantom study and a patient study. RESULTS: The results of the simulation study demonstrate that, in surface-based registration, the triple-registration method results in a smaller ictal-interictal SPECT registration error than the pair-wise registration method (P<0.05) for a range of values of the cost-function parameter. However, the improved registration error is still larger than that obtained by the normalized mutual information method (P<0.001), which is a voxel-based registration algorithm. The phantom and patient studies reveal no observable difference between registration results. CONCLUSIONS: Although the improved accuracy of triple registration is slightly worse than voxel-based registration, it will soon be possible to apply the results of this study in research utilizing the triple-registration principle to improving voxel-based results of ictal-interictal registration.

Algorithms↗

PubMed2005

Sampling of sentinel lymph nodes: a simulation study.

OBJECTIVE: To analyze the effects of various slicing schemes on the detection of metastases in lymph nodes. STUDY DESIGN: Use of an advanced computer simulation tool. RESULTS: Bisectioning along the longitudinal axis is an inadequate approach. Slicing ellipsoid lymph nodes along their longitudinal axis also results in a lower rate of detecting metastases since metastatic deposits have a predilection to localize subcapsularly. CONCLUSION: Ellipsoid lymph nodes must be sliced perpendicular to the longest axis to increase the rate of detecting metastases.

Computer Simulation↗