Biomedical and Bioinformatics Challenges for Computer Science (BBC) Session 1

Time and Date: 16:30 - 18:10 on 13th June 2018

Room: 0.4

Chair: Mario Cannataro

38 Parallelization of an algorithm for automatic classification of medical data [abstract]
Abstract: In this paper, we present the optimization and parallelization of a state-of-the-art algorithm for automatic classification, aiming to perform real time classification of clinical data. The parallelization has been carried out so that the algorithm can be used in real time in standard computers, or in high performance computing servers. The fastest versions have been obtained carrying out most of the computations in Graphics processing Units (GPUs). The algorithms obtained have been tested in a case of automatic classification of electroencephalographic signals from patients.
Victor M. Garcia-Molla, Addisson Salazar, Gonzalo Safont, Antonio M. Vidal and Luis Vergara
86 The Chain Alignment Problem [abstract]
Abstract: This paper introduces two new combinatorial optimization problems involving strings, namely, the Chain Alignment Problem, and a multiple version of it, the Multiple Chain Alignment Problem. For the first problem, a polynomial-time algorithm using dynamic program- ming is presented, and for the second one, a proof of its N P-hardness is provided and some heuristics are proposed for it. The applicability of both problems here introduced is attested by their good results when modeling the Gene Identification Problem.
Leandro I. S. De Lima and Said Sadique Adi
146 Comparing Deep and Machine Learning approaches in bioinformatics: a miRNA-target prediction case study [abstract]
Abstract: MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field.
Mauro Castelli, Stefano Beretta, Valentina Giansanti and Ivan Merelli
158 Automated epileptic seizure detection method based on the multi-attribute EEG feature pool and mRMR feature selection method [abstract]
Abstract: Electroencephalogram (EEG) signals reveal many crucial hidden attributes of the human brain. Classification based on EEG-related features can be used to detect brain-related diseases, especially epilepsy. The quality of EEG-related features is directly related to the performance of automated epileptic seizure detection. Therefore, finding prominent features bears importance in the study of automated epileptic seizure detection. In this paper, a novel method is proposed to automatically detect epileptic seizure. This work proposes a novel time-frequency-domain feature named global volatility index (GVIX) to measure holistic signal fluctuation in wavelet coefficients and original time-series signals. Afterwards, the multi-attribute EEG feature pool is constructed by combining time-frequency-domain features, time-domain features, nonlinear features, and entropy-based features. Minimum redundancy maximum relevance (mRMR) is then introduced to select the most prominent features. Results in this study indicate that this method performs better than others for epileptic seizure detection using an identical dataset, and that our proposed GVIX is a prominent feature in automated epileptic seizure detection.
Bo Miao, Junling Gun, Liangliang Zhang, Qingfang Meng and Yulin Zhang