EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.Comment: 11 pages, 6 figure

Improving classification of epileptic and non-epileptic EEG events by feature selection

This is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.Correctly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.Peer reviewe

A Comparative Study of Biomechanical Simulators in Deformable Registration of Brain Tumor Images

Simulating the brain tissue deformation caused by tumor growth has been found to aid the deformable registration of brain tumor images. In this paper, we evaluate the impact that different biomechanical simulators have on the accuracy of deformable registration. We use two alternative frameworks for biomechanical simulations of mass effect in 3-D magnetic resonance (MR) brain images. The first one is based on a finite-element model of nonlinear elasticity and unstructured meshes using the commercial software package ABAQUS. The second one employs incremental linear elasticity and regular grids in a fictitious domain method. In practice, biomechanical simulations via the second approach may be at least ten times faster. Landmarks error and visual examination of the coregistered images indicate that the two alternative frameworks for biomechanical simulations lead to comparable results of deformable registration. Thus, the computationally less expensive biomechanical simulator offers a practical alternative for registration purposes

Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients

This is the accepted manuscript version of the following article: Iosif Mporas, “Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients”, Expert Systems with Applications, Vol. 42(6), December 2014. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0957417414007763?via%3Dihub © 2014 Elsevier Ltd. All rights reserved.In this paper a seizure detector using EEG and ECG signals, as a module of a healthcare system, is presented. Specifically, the module is based on short-time analysis with time-domain and frequency-domain features and classification using support vector machines. The seizure detection module was evaluated on three subjects with diagnosed idiopathic generalized epilepsy manifested with absences. The achieved seizure detection accuracy was approximately 90% for all evaluated subjects. Feature ranking investigation and evaluation of the seizure detection module using subsets of features showed that the feature vector composed of approximately the 65%-best ranked parameters provides a good trade-off between computational demands and accuracy. This configurable architecture allows the seizure detection module to operate as part of a healthcare system in offline mode as well as in online mode, where real-time performance is needed.Peer reviewe

Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images

Rationale and Objectives: Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. MRI is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques like perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multi-parametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods, in order to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter. Materials and Methods: Five structural MR sequences, namely, B0, Diffusion Weighted Images, FLAIR, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI, i.e., fractional anisotropy and apparent diffusion coefficient, are used to create an intensity-based tissue profile. This is incorporated into a non-linear pattern classification technique to create a multi-parametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high grade neoplasms who have not received any therapy prior to imaging. Results: Preliminary results demonstrate that this multi-parametric tissue characterization helps to better differentiate between neoplasm, edema and healthy tissue, and to identify tissue that is likely progress to neoplasm in the future. This has been validated on expert assessed tissue. Conclusion: This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence

Insights into the molecular mechanisms of stress and inflammation in ageing and frailty of the elderly

Frailty is a natural state of physical, cognitive and mental decline that is expected in the elderly. The role of inflammation in the pathogenesis of frailty has been hypothesized, and so far many studies have been performed in order to understand the mechanism of action underlying this association. Recent studies support this hypothesis and show a clear association between inflammation, frailty, and age-related disease. Chronic inflammation is key pathophysiologic process that contributes to the frailty directly and indirectly through other intermediate physiologic systems, such as the musculoskeletal, endocrine, and hematologic systems. The complex multifactorial etiologies of frailty also include obesity and other age-related specific diseases. Herein, we investigate the link between chronic inflammation and frailty of the older people. In particular, we present an up-to-date review of the role of cytokines, interleukins, cardiovascular abnormalities, chronic high blood pressure, hyperlipidemia and diabetes in relation to the severity of frailty in the elderly

Deformable group-wise registration using a physiological model: application to DIffusion-Weighted MRI

International audienceIntensity variations can often be described by a physiological or temporal model applied on a voxel-wise basis across a group of images. However the voxel correspondence might be unknown, imposing the need for a group-wise deformable registration coupled with the computation of the model parameters. In this paper we propose a group-wise registration method of medical images that incorporates the temporal dimension (reflecting the change of signal amplitude) of the acquisition process. Consistency on the spatiotemporal physiological model, as well as deformation smoothness, is imposed in order to produce anatomically meaningful representations of the 3D images. The performance of the proposed method is compared to two different group-wise registration approaches; one that penalizes the absolute differences in the intensities and one that penalizes the intensity range among the images on corresponding regions. We chose as an application paradigm the registration of diffusion-weighted magnetic resonance (DW-MR) images for the evaluation of patients with lymphomas. A dataset consisting of 25 patients, each scanned with 3 " b values " , was used to evaluate the method's accuracy. The proposed registration method outperfomed the other two registration approaches, making it a very promising method for highlighting the importance of DWI as an imaging biomarker

STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis: MR-based breast tumor diagnosis

The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97