71 citations · 137 across the 8 of their papers we have counts for
8 papers
Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations
Hazem Alsamkary, Mohamed Elshaffei, Mohamed Elkerdawy +1
Protein language models (PLMs) have emerged as powerful tools to detect complex patterns of protein sequences. However, the capability of PLMs to fully capture information on prote…
Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction
Hazem Alsamkary, Mohamed Elshaffei, Mohamed Soudy +5
Protein-protein interactions (PPIs) are fundamental to numerous cellular processes, and their characterization is vital for understanding disease mechanisms and guiding drug discov…
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin +4
As opposed to scaling-up protein language models (PLMs), we seek improving performance via protein-specific optimization. Although the proportionality between the language model si…
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar, Wei Ding, Llion Jones +6
Currently, a growing number of mature natural language processing applications make people's life more convenient. Such applications are built by source code - the language in soft…
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago +9
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers…
Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification
Ahmed Elnaggar, Christoph Gebendorfer, Ingo Glaser +1
The digitalization of the legal domain has been ongoing for a couple of years. In that process, the application of different machine learning (ML) techniques is crucial. Tasks such…
Named-Entity Linking Using Deep Learning For Legal Documents: A Transfer Learning Approach
Ahmed Elnaggar, Robin Otto, Florian Matthes
In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is c…
Stop Illegal Comments: A Multi-Task Deep Learning Approach
Ahmed Elnaggar, Bernhard Waltl, Ingo Glaser +3
Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep lea…