Michaela Areti Zervou

Bird-MML: A Multimodal Dataset for Audio-Visual Complementarity

Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras, Maarten De Vos et autres

Bird-MML is a synthetic multimodal dataset designed to study cross-modal representation learning and multimodal complementarity across vision, audio, and text. Despite substantial progress in multimodal learning, there remains a lack of standardized datasets that support the evaluation of both pairwise and higher-order …

Transfer learning on protein language models improves antimicrobial peptide classification

Michaela Areti Zervou, Yannis Pantazis

Antimicrobial peptides (AMPs) are essential components of the innate immune system in humans and other organisms, exhibiting potent activity against a broad spectrum of pathogens. Their potential therapeutic applications, particularly in combating antibiotic resistance, have rendered AMP classification a vital task in …

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Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design

Michaela Areti Zervou, Effrosyni Doutsi, Yannis Pantazis, Panagiotis Tsakalides

The development of antimicrobial peptides (AMPs) presents a promising approach to addressing antibiotic-resistant pathogens. Computational methods, such as Feedback Generative Adversarial Networks (FBGANs), have demonstrated strong performance in optimizing AMP design. FBGAN operates as a classifier-guided Generative Adversarial Network (GAN), refining training …

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Transfer Learning on Protein Language Models Improves Antimicrobial Peptide Classification

Michaela Areti Zervou, Yannis Pantazis

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De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks

Michaela Areti Zervou, Effrosyni Doutsi, Yannis Pantazis, Panagiotis Tsakalides

Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A …

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Multitask Classification of Antimicrobial Peptides for Simultaneous Assessment of Antimicrobial Property and Structural Fold

Michaela Areti Zervou, Effrosyni Doutsi, Yannis Pantazis, Panagiotis Tsakalides

Antimicrobial peptides (AMPs) play a significant role in guiding drug design, advancing targeted therapies, and cancer treatment research. The function of peptides is highly associated with their three-dimensional structure. AMPs particularly favor alpha-helical structures, or alpha-folds, due to their ability to disrupt …

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Efficient Protein Structural Class Prediction Via Chaos Game Representation and Recurrent Neural Networks

Michaela Areti Zervou, Effrosyni Doutsi, Panagiotis Tsakalides

Predicting the structural class of a protein from its amino acid sequence is among the most significant problems in bioinformatics, especially for proteins with a low sequence similarity. While current methods using recurrent neural networks achieve a notable accuracy in this task, …

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Secondary Structure Classification of Low-homology Proteins with Graph Neural Networks

Michaela Areti Zervou, Effrosyni Doutsi, Panagiotis Tsakalides

Acquiring knowledge of the dynamics and proper-ties that force each protein into a unique secondary structure is highly important in medicine and biotechnology. The majority of existing prediction methods rely on complicated processes to ex-tract numerous representative features that are later paired …

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Visibility Graph Network of Multidimensional Time Series Data for Protein Structure Classification

Michaela Areti Zervou, Effrosyni Doutsi, Panagiotis Tsakalides

In the last decades, many studies have explored the potential of utilizing complex network approaches to characterize time series generated from dynamical systems. Along these lines, Visibility Graph (VG) and Horizontal Visibility Graph (HVG) networks have contributed to an important yet difficult …

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Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs

Michaela Areti Zervou, Effrosyni Doutsi, Pavlos Pavlidis, Panagiotis Tsakalides

MOTIVATION: Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a …

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Efficient Dynamic Analysis of Low-similarity Proteins for Structural Class Prediction

Michaela Areti Zervou, Effrosyni Doutsi, Pavlos Pavlidis, Panagiotis Tsakalides

Prediction of protein structural classes from amino acid sequences is a challenging problem as it is profitable for analyzing protein function, interactions, and regulation. The majority of existing prediction methods for low-homology sequences utilize numerous amount of features and require an exhausting …

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Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs

Michaela Areti Zervou, Effrosyni Doutsi, Pavlos Pavlidis, Panagiotis Tsakalides

Abstract Motivation Protein structure prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a …

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