You can also find my articles on my Google Scholar profile.
Xiao Fei, Michail Chatzianastasis, Sarah Almeida Carneiro, Hadi Abdine, Lawrence P. Petalidis, Michalis Vazirgiannis
We propose Prot2Text, which predicts a protein function’s in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder...
Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, Michalis Vazirgiannis
We propose Prot2Text, which predicts a protein function’s in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder...
Michail Chatzianastasis, Michalis Vazirgiannis, Zijun Zang
We introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data.
Michail Chatzianastasis, Loukas Ilias, Dimitris Askounis, Michalis Vazirgiannis
We propose a multimodal deep learning approach to combine speech and text modalities for recognizing Alzheimer’s dementia (AD) using Neural Architecture Search.
Michail Chatzianastasis, Johannes Lutzeyer, George Dasoulas, Michalis Vazirgiannis
We introduce the Graph Ordering Attention (GOAT) layer, a novel GNN component that learns local node orderings via an attention mechanism and processes the ordered representations using a recurrent neural network aggregator.
Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis
In this paper, we define a distance function between nodes which is based on the hierarchy produced by the WL algorithm, and propose a model that learns representations which preserve those distances between nodes. Since the emerging hie...
Michail Chatzianastasis, Giannis Nikolentzos, Michalis Vazirgiannis
We propose a new technique that can be incorporated into any graph attention model to encourage higher attention scores between nodes that share the same class label.
Michail Chatzianastasis, Giannis Nikolentzos, Michalis Vazirgiannis
We propose a node embedding algorithm for the problem of drug repurposing. The proposed algorithm learns node representations that capture the influence of nodes in the biological network by learning a mass term for each node along with ...
Michail Chatzianastasis, George Dasoulas, Georgios Siolas, Michalis Vazirgiannis
We propose the replacement of fixed operator encoding with learnable representations in the optimization process.