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General General

Knowledge Discovery on IoT-Enabled mHealth Applications.

In Advances in experimental medicine and biology

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Menychtas Andreas, Tsanakas Panayiotis, Maglogiannis Ilias

2020

Activity tracking, Biosignals, Data analytics, Data harmonization, Gateway, IoT, Knowledge discovery, Quantified-Self, mHealth

General General

Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances.

In Advances in experimental medicine and biology

Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.

Cheirdaris Dionysios G

2020

Artificial neural networks (ANNs), Computer-aided drug design (CADD), Molecular predictors, Quantitative structure-activity relationship (QSAR) models

General General

A Deep Learning Approach for Human Action Recognition Using Skeletal Information.

In Advances in experimental medicine and biology

In this paper we present an approach toward human action detection for activities of daily living (ADLs) that uses a convolutional neural network (CNN). The network is trained on discrete Fourier transform (DFT) images that result from raw sensor readings, i.e., each human action is ultimately described by an image. More specifically, we work using 3D skeletal positions of human joints, which originate from processing of raw RGB sequences enhanced by depth information. The motion of each joint may be described by a combination of three 1D signals, representing its coefficients into a 3D Euclidean space. All such signals from a set of human joints are concatenated to form an image, which is then transformed by DFT and is used for training and evaluation of a CNN. We evaluate our approach using a publicly available challenging dataset of human actions that may involve one or more body parts simultaneously and for two sets of actions which resemble common ADLs.

Mathe Eirini, Maniatis Apostolos, Spyrou Evaggelos, Mylonas Phivos

2020

Activities of daily living, Convolutional neural networks, Human action recognition

General General

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

In Advances in experimental medicine and biology

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.

Skolariki Konstantina, Terrera Graciella Muniz, Danso Samuel

2020

Alzheimer’s biomarkers, Decision trees, MCI-to-AD progression, Machine learning, Naive Bayes, Support vector machine

General General

Neuroeducation and Computer Programming: A Review.

In Advances in experimental medicine and biology

Over the past 5 years, a significant number of studies focused on computer programming and code writing (software development, code comprehension, program debugging, code optimization, developer training), using the capabilities of brain imaging techniques and of biomarkers. With the use of the aforementioned techniques, researchers have explored the role of programming experience and knowledge, the relation between coding and writing, and the possibilities of improving program debugging with machine learning techniques. In this paper, a review of existing literature and discussion of research issues that should be examined in the future are explored. Research may link the neuroscientific field with training issues in programming, so as to contribute to the learning process.

Giannopoulou Panagiota, Papalaskari Mary-Angela, Doukakis Spyridon

2020

Computer programming education, Mobile applications, Neuroeducation, Training

Surgery Surgery

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

In Advances in experimental medicine and biology

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Papageorgiou Louis, Maroulis Dimitris, Chrousos George P, Eliopoulos Elias, Vlachakis Dimitrios

2020

Antibodies, Antibody drug conjugates, Antibody-antigen complexes, Classification scheme, Clustering, Immunology