M. Ughetto
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View article: Representation Learning of Human Disease Mechanisms for a Foundation Model in Rare and Common Diseases
Representation Learning of Human Disease Mechanisms for a Foundation Model in Rare and Common Diseases Open
The limited amount of data available renders it challenging to characterize which biological processes are relevant to a rare disease. Hence, there is a need to leverage the knowledge of disease pathogenesis and treatment from the wider di…
View article: Machine Learning-Based Interpretation of Optical Properties of Colloidal Gold with Convolutional Neural Networks
Machine Learning-Based Interpretation of Optical Properties of Colloidal Gold with Convolutional Neural Networks Open
Gold nanoparticles are used in a range of applications, but their properties depend on their shape, size, and polydispersity. A quick, easy, and accurate characterization of the particles is therefore of high importance, especially in flow…
View article: Community-aware explanations in knowledge graphs with XP-GNN
Community-aware explanations in knowledge graphs with XP-GNN Open
Machine learning applications for the drug discovery pipeline have exponentially increased in the last few years. An example of these applications is the biological Knowledge Graph. These graphs represent biological entities and the relati…
View article: SuSpect3: A C++ code for the supersymmetric and Higgs particle spectrum of the MSSM
SuSpect3: A C++ code for the supersymmetric and Higgs particle spectrum of the MSSM Open
View article: SuSpect3: A C++ Code for the Supersymmetric and Higgs Particle Spectrum of the MSSM
SuSpect3: A C++ Code for the Supersymmetric and Higgs Particle Spectrum of the MSSM Open
We present the program SuSpect3 that calculates the masses and couplings of the Higgs and supersymmetric particles predicted by the Minimal Supersymmetric Standard Model (MSSM). The model is implemented in both its non-constrained version,…
View article: A Unified View of Relational Deep Learning for Drug Pair Scoring
A Unified View of Relational Deep Learning for Drug Pair Scoring Open
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction, and combination therapy design tasks have been proposed. Here, we present a unified theoret…
View article: Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer
Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer Open
View article: ChemicalX: A Deep Learning Library for Drug Pair Scoring
ChemicalX: A Deep Learning Library for Drug Pair Scoring Open
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scor…
View article: A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction
A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction Open
In recent years, numerous machine learning models which attempt to solve
polypharmacy side effect identification, drug-drug interaction prediction and
combination therapy design tasks have been proposed. Here, we present a unified
theoreti…
View article: Biological Insights Knowledge Graph: an integrated knowledge graph to support drug development
Biological Insights Knowledge Graph: an integrated knowledge graph to support drug development Open
The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug…
View article: Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer
Knowledge Graph-based Recommendation Framework Identifies Novel Drivers of Resistance in EGFR mutant Non-small Cell Lung Cancer Open
Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRI…
View article: Study of energy response and resolution of the ATLAS Tile Calorimeter to hadrons of energies from 16 to 30 GeV
Study of energy response and resolution of the ATLAS Tile Calorimeter to hadrons of energies from 16 to 30 GeV Open
View article: Go-HEP: libraries for High Energy Physics analyses in Go
Go-HEP: libraries for High Energy Physics analyses in Go Open
View article: ATLAS Supersymmetry Searches
ATLAS Supersymmetry Searches Open
Despite the absence of experimental evidence, weak scale supersymmetry remains one of the best motivated and studied Standard Model extensions. This talk summarises recent ATLAS results for searches for supersymmetric (SUSY) particles, wit…
View article: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="italic">Z</mml:mi></mml:math>boson production in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:mi>Pb</mml:mi></mml:mrow></mml:math>collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msqrt><mml:msub><mml:mi>s</mml:mi><mml:mi mathvariant="italic">NN</mml:mi></mml:msub></mml:msqrt><mml:mo>=</mml:mo><mml:mn>5.02</mml:mn></mml:mrow></mml:math>TeV measured with the ATLAS detector
boson production incollisions atTeV measured with the ATLAS detector Open
With this research, the ATLAS Collaboration measures the inclusive production of Z bosons via their decays into electron and muon pairs in p + Pb collisions at \\(\\sqrt{s_{\\mathrm{NN}}} = 5.02\\) TeV at the Large Hadron Collider. …
View article: Technical Progress of the BESS Spectrometer
Technical Progress of the BESS Spectrometer Open