The Experts below are selected from a list of 26496 Experts worldwide ranked by ideXlab platform
Arne Elofsson - One of the best experts on this subject based on the ideXlab platform.
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using Multiple Templates to improve quality of homology models in automated homology modeling
Protein Science, 2008Co-Authors: Per Larsson, Bjorn Wallner, Erik Lindahl, Arne ElofssonAbstract:When researchers build high-quality models of protein structure from sequence homology, it is today common to use several alternative target-Template alignments. Several methods can, at least in theory, utilize information from Multiple Templates, and many examples of improved model quality have been reported. However, to our knowledge, thus far no study has shown that automatic inclusion of Multiple alignments is guaranteed to improve models without artifacts. Here, we have carried out a systematic investigation of the potential of Multiple Templates to improving homology model quality. We have used test sets consisting of targets from both recent CASP experiments and a larger reference set. In addition to Modeller and Nest, a new method (Pfrag) for Multiple Template-based modeling is used, based on the segment-matching algorithm from Levitt's SegMod program. Our results show that all programs can produce multi-Template models better than any of the single-Template models, but a large part of the improvement is simply due to extension of the models. Most of the remaining improved cases were produced by Modeller. The most important factor is the existence of high-quality single-sequence input alignments. Because of the existence of models that are worse than any of the top single-Template models, the average model quality does not improve significantly. However, by ranking models with a model quality assessment program such as ProQ, the average quality is improved by ∼5% in the CASP7 test set.
Neda Bernasconi - One of the best experts on this subject based on the ideXlab platform.
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Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling
Frontiers Media S.A., 2018Co-Authors: Hosung Kim, Benoit Caldairou, Andrea Bernasconi, Neda BernasconiAbstract:Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent Multiple-Template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large Template library, as segmentation suffers when the boundaries of Template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of Templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal Template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable Template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-Template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-Template segmentation, HybridMulti could maintain accurate performance even with a 50% Template library size
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Image_1_Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling.jpg
2018Co-Authors: Hosung Kim, Benoit Caldairou, Andrea Bernasconi, Neda BernasconiAbstract:Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent Multiple-Template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large Template library, as segmentation suffers when the boundaries of Template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of Templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal Template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable Template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-Template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-Template segmentation, HybridMulti could maintain accurate performance even with a 50% Template library size.
Hosung Kim - One of the best experts on this subject based on the ideXlab platform.
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Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling
Frontiers Media S.A., 2018Co-Authors: Hosung Kim, Benoit Caldairou, Andrea Bernasconi, Neda BernasconiAbstract:Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent Multiple-Template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large Template library, as segmentation suffers when the boundaries of Template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of Templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal Template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable Template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-Template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-Template segmentation, HybridMulti could maintain accurate performance even with a 50% Template library size
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Image_1_Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling.jpg
2018Co-Authors: Hosung Kim, Benoit Caldairou, Andrea Bernasconi, Neda BernasconiAbstract:Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent Multiple-Template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large Template library, as segmentation suffers when the boundaries of Template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of Templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal Template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable Template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-Template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-Template segmentation, HybridMulti could maintain accurate performance even with a 50% Template library size.
Jürgen Bajorath - One of the best experts on this subject based on the ideXlab platform.
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comparison of 2d fingerprint methods for Multiple Template similarity searching on compound activity classes of increasing structural diversity
ChemMedChem, 2007Co-Authors: Andrea Tovar, Hanna Eckert, Jürgen BajorathAbstract:We studied the similarity search performance of differently designed molecular fingerprints using Multiple reference structures and different search strategies. For this purpose, nine compound activity classes were assembled that exclusively consisted of molecules with different core structures and that represented different levels of intra-class structural diversity. Thus, there was a strict one-to-one correspondence between test compounds and core structures. Analysis of unique core structures was found to be a better measure of class diversity than distributions of simplified scaffolds. On increasingly diverse classes, a trainable fingerprint using a unique search strategy performed better than others tested herein. Overall, clear preferences were detected for nearest-neighbor search strategies over fingerprint-averaging techniques. Nearest-neighbor searching that relied on selecting database compounds most similar to one of the reference structures often improved compound recovery over other averaging methods, but at the cost of decreasing the ability to detect hits that were structurally distinct from reference molecules.
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bayesian interpretation of a distance function for navigating high dimensional descriptor spaces
Journal of Chemical Information and Modeling, 2007Co-Authors: Martin Vogt, Jeffrey W Godden, Jürgen BajorathAbstract:A distance function to analyze molecular similarity relationships in high-dimensional descriptor spaces and focus search calculations on "active subspaces" is defined in Bayesian terms. As a measure of similarity, database compounds are ranked according to their distance from the center of a subspace formed by known active molecules. From a Bayesian point of view, distance calculations are transformed into a "log-odds" estimate. Following this approach, maximizing the likelihood of a compound to be active corresponds to minimizing the distance from the center of an active subspace. Since the methodology generates a ranking of database molecules according to decreasing similarity to Template compounds, it can be conveniently compared to similarity search tools, and the Bayesian function is found to compare favorably to two standard fingerprints in Multiple Template-based database searching.
Per Larsson - One of the best experts on this subject based on the ideXlab platform.
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using Multiple Templates to improve quality of homology models in automated homology modeling
Protein Science, 2008Co-Authors: Per Larsson, Bjorn Wallner, Erik Lindahl, Arne ElofssonAbstract:When researchers build high-quality models of protein structure from sequence homology, it is today common to use several alternative target-Template alignments. Several methods can, at least in theory, utilize information from Multiple Templates, and many examples of improved model quality have been reported. However, to our knowledge, thus far no study has shown that automatic inclusion of Multiple alignments is guaranteed to improve models without artifacts. Here, we have carried out a systematic investigation of the potential of Multiple Templates to improving homology model quality. We have used test sets consisting of targets from both recent CASP experiments and a larger reference set. In addition to Modeller and Nest, a new method (Pfrag) for Multiple Template-based modeling is used, based on the segment-matching algorithm from Levitt's SegMod program. Our results show that all programs can produce multi-Template models better than any of the single-Template models, but a large part of the improvement is simply due to extension of the models. Most of the remaining improved cases were produced by Modeller. The most important factor is the existence of high-quality single-sequence input alignments. Because of the existence of models that are worse than any of the top single-Template models, the average model quality does not improve significantly. However, by ranking models with a model quality assessment program such as ProQ, the average quality is improved by ∼5% in the CASP7 test set.
