Publications associées à Can survival prediction be improved by merging gene expression data sets?

Tumor-educated Gr1+CD11b+cells drive breast cancer metastasis via OSM/IL-6/JAK-induced cancer cell plasticity

Annamaria Kauzlaric, Nadine Fournier, Rachel Marcone, Manon Bulliard, Yu-Ting Huang

Cancer cell plasticity contributes to therapy resistance and metastasis, which represent the main causes of cancer-related death, including in breast cancer. The tumor microenvironment drives cancer cell plasticity and metastasis, and unraveling the underl ...

Amer Soc Clinical Investigation Inc2024

Unveiling breast cancer metastasis through an advanced X-ray imaging approach

Marianne Liebi

Breast cancer is a significant global health burden, causing a substantial number of deaths. Systemic metastatic tumour cell dissemination is a major cause of poor outcomes. Understanding the mechanisms underlying metastasis is crucial for effective interv ...

Nature Portfolio2024

Is There a Special Role for Ovarian Hormones in the Pathogenesis of Lobular Carcinoma?

Cathrin Brisken, Georgios Sflomos

Lobular carcinoma represent the most common special histological subtype of breast cancer, with the majority classed as hormone receptor positive. Rates of invasive lobular carcinoma in postmenopausal women have been seen to increase globally, while other ...

Endocrine Soc2024

Cytokine-armed dendritic cell progenitors for antigen-agnostic cancer immunotherapy

Ali Ghasemi

Dendritic cells (DCs) are specialized myeloid cells with the ability to uptake, process, and present antigens to T lymphocytes. They also generate cytokine and chemokine gradients that regulate immune cell trafficking, activation, and function. Monocyte-de ...

EPFL2024

Data and scripts for the RaFSIP scheme

Athanasios Nenes, Paraskevi Georgakaki

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Zenodo2024

The role of LRRK2 in lung adenocarcinoma

Aspasia Gkasti

Lung cancer is the leading cause of cancer-related deaths worldwide and the most common lung cancer subtype is lung adenocarcinoma (LUAD). Frequently mutated genes involve activating mutations in KRAS and loss of function mutations in TP53. LUADs primarily ...

EPFL2024

6-diazo-5-oxo-l-norleucine prodrugs

Xile Hu, Li Tang, Céline Jasmin Prange

The present invention relates to 6-diazo-5-oxo-L-norleucine prodrugs having reduced toxicity and increased tumor specificity and to their therapeutic use for treating cancer. ...

2024

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

Association between endocrine adjuvant therapy intake timing and disease-free survival in patients with high-risk early breast cancer: results of a sub-study of the UCBG- UNIRAD trial

Fabrice André, Elise Hélène Dumas

Background Circadian rhythms regulate cellular physiology and could in fl uence the ef fi cacy of endocrine therapy (ET) in breast cancer (BC). We prospectively tested this hypothesis within the UNIRAD adjuvant phase III trial (NCT01805271). Methods 1278 p ...

Elsevier2024

Androgen Receptor Signaling in in vivo models of Estrogen Receptor-positive Breast Cancer and Normal Breast Epithelium

Andrea Agnoletto

Steroid hormone receptors (HRs) are ligand-activated transcription factors that play a pivotal role in breast development and carcinogenesis. It is well established that estrogen receptor (ER) signaling is a major proliferative driver in estrogen receptor ...

EPFL2024

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Andreas Mortensen, Léa Deillon, Alejandra Inés Slagter, Eva Luisa Vogt, David Hernandez Escobar, Jonathan Aristya Setyadji

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Zenodo2024

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Lijing Xin, Yan Lin, Wei Ye, Yubo Zhao

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabo ...

Nature Portfolio2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Data-driven statistical optimization of a groundwater monitoring network

Andrea Rinaldo, Gianluca Botter

We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

Elsevier2024

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Hao Lu, Chao Hu, Olga Fink

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

EPFL2024

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Alireza Modirshanechi

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024