Search results for " recognition"
showing 10 items of 3220 documents
Improving Docking Performance Using Negative Image-Based Rescoring
2017
Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing th…
Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy
2017
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive b…
Automatic detection of hemangiomas using unsupervised segmentation of regions of interest
2016
In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…
Deep learning network for exploiting positional information in nucleosome related sequences
2017
A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…
Topographic Independent Component Analysis reveals random scrambling of orientation in visual space
2017
Neurons at primary visual cortex (V1) in humans and other species are edge filters organized in orientation maps. In these maps, neurons with similar orientation preference are clustered together in iso-orientation domains. These maps have two fundamental properties: (1) retinotopy, i.e. correspondence between displacements at the image space and displacements at the cortical surface, and (2) a trade-off between good coverage of the visual field with all orientations and continuity of iso-orientation domains in the cortical space. There is an active debate on the origin of these locally continuous maps. While most of the existing descriptions take purely geometric/mechanistic approaches whi…
SpCLUST: Towards a fast and reliable clustering for potentially divergent biological sequences
2019
International audience; This paper presents SpCLUST, a new C++ package that takes a list of sequences as input, aligns them with MUSCLE, computes their similarity matrix in parallel and then performs the clustering. SpCLUST extends a previously released software by integrating additional scoring matrices which enables it to cover the clustering of amino-acid sequences. The similarity matrix is now computed in parallel according to the master/slave distributed architecture, using MPI. Performance analysis, realized on two real datasets of 100 nucleotide sequences and 1049 amino-acids ones, show that the resulting library substantially outperforms the original Python package. The proposed pac…
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images -- A Cross-Site Robustness Assessment
2017
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the…
Brain-like large scale cognitive networks and dynamics
2018
A new approach to the study of the brain and its functions known as Human Connectomics has been recently established. Starting from magnetic resonance images (MRI) of brain scans, it is possible to identify the fibers that link brain areas and to build an adjacency matrix that connects these areas, thus creating the brain connectome. The topology of these networks provides a lot of information about the organizational structure of the brain (both structural and functional). Nevertheless this knowledge is rarely used to investigate the possible emerging brain dynamics linked to cognitive functions. In this work, we implement finite state models on neural networks to display the outcoming bra…
A framework for data-driven adaptive GUI generation based on DICOM
2018
Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. As a consequence, the physician may be affected by cognitive overload and visual stress causing a degradation of performances, mainly due to unuseful widgets. In clinical environments, a GUI must represent a sequence of steps for image investi…
Informational and linguistic analysis of large genomic sequence collections via efficient Hadoop cluster algorithms
2018
Abstract Motivation Information theoretic and compositional/linguistic analysis of genomes have a central role in bioinformatics, even more so since the associated methodologies are becoming very valuable also for epigenomic and meta-genomic studies. The kernel of those methods is based on the collection of k-mer statistics, i.e. how many times each k-mer in {A,C,G,T}k occurs in a DNA sequence. Although this problem is computationally very simple and efficiently solvable on a conventional computer, the sheer amount of data available now in applications demands to resort to parallel and distributed computing. Indeed, those type of algorithms have been developed to collect k-mer statistics in…