[Nov 25th 2020] LabMeeting: LSTM for the prediction of the translation speed based on Ribosome Profiling

Caterina Graziani (University of Siena) Nov 25, 2020 – 11:00 – 12:30 AM Conference Meeting Description Ribosome profiling (Ribo-seq profiling) is a powerful tool for studying the translational control of gene expression. The translational profile of an RNA sequence is estimated by counting the occurrences of its subsequences in ribosome footprints. The translational speed of […]

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[Nov 18th 2020] LabMeeting: Towards the determination of thresholds in neural networks

Giuseppe Alessio D’Inverno (University of Siena) Nov 18, 2020 – 11:45 – 12:30 AM Conference Meeting Description Neural networks are nowadays one of the hottest topics in Applied Mathematics and, from Bioinformatics to Artificial Intelligence, their use spreads over several scientific disciplines. Beside the analytical approach, which is predominant in the literature, a new geometric […]

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[Nov 18th 2020] LabMeeting: Neural networks for processing heterogeneous graphs

Federica Baccini (University of Florence) Nov 18, 2020 – 11:00 – 11:45 AM Conference Meeting Description In this talk, I will introduce the problem of processing heterogeneous graphs (HETGs), i.e. graphs where nodes represent different entities, and edges denote multiple relation types. Despite heterogeneous graphs are able to model a broad variety of real world […]

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[Nov 11th 2020] LabMeeting: Linked Credibility Reviews for Explainable Misinformation Detection

José Manuel Gomez-Perez Nov 11, 2020 – 11:00 – 11:45 AM Conference Meeting Description In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based in deep […]

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[Nov 11th 2020] LabMeeting: Robust Prostate Cancer Classification with Siamese Neural Network

Alberto Rossi (University of Florence) Nov 11, 2020 – 11:00 – 11:45 AM Conference Meeting Description Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with […]

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[Nov 4th 2020] LabMeeting: Introduction to Transformers

Andrea Zugarini (University of Florence) Nov 4, 2020 – 11:00 – 11:45 AM Conference Meeting Description In the last decade most of the advances in NLP were achieved by learning textual representations with unsupervised Language Modeling-related tasks on large corpora. Recently, this principle was pushed even further with transformers, where the scales of datasets and […]

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[Oct 21th 2020] LabMeeting: SAAMBE-SEQ: A Sequence-based Method for Predicting Mutation Effect on Protein-protein Binding Affinity

Anna Visibelli (University of Siena) Oct 21, 2020 – 11:00 – 11:45 AM Conference Meeting Description Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the […]

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[Oct 7th 2020] LabMeeting: Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

Giorgio Ciano (University of Florence) Oct 7, 2020 – 11:00 – 11:45 AM Conference Meeting Description Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research […]

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[Sep 30th 2020] LabMeeting: Imitation Learning – predicting goal-directed scanpaths

Lapo Faggi (University of Florence) Sep 30, 2020 – 11:00 AM Conference Meeting Description In this seminar, some recent imitation learning algorithms will be presented. Imitation learning, also known as learning from demonstrations, is a powerful and practical alternative to reinforcement learning for learning sequential decision-making policies without the need of defining any (hand-crafted) reward […]

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