Št. zadetkov: 395
Izvirni znanstveni članek
Oznake:
osimertinib;raziskava GioTag;GioTag study;
Aim: Final overall survival (OS) and time on treatment analysis of patients with EGFR mutation-positive non-small-cell lung cancer (NSCLC) who received sequential afatinib and osimertinib. Patients & methods: Patients (n = 203) had T790M-positive disease following first-line afatinib and started osi ...
Leto:
2020
Vir:
Digitalni repozitorij raziskovalnih organizacij Slovenije
Maximilian J Hochmair,
Alessandro Morabito,
Desiree Hao,
Cheng-Ta Yang,
Ross A Soo,
James C-H Yang,
Rasim Gucalp,
Balazs Halmos,
Lara Wang,
Angela Märten,
Tanja Čufer
Izvirni znanstveni članek
Oznake:
osimertinib;raziskava GioTag;GioTag study;
Aims: Overall survival (OS) and updated time to treatment failure (TTF) analysis of patients with EGFR mutation-positive (Del19, L858R) non-small-cell lung cancer who received sequential afatinib/osimertinib in the real-world GioTag study. Patients & methods: Patients had T790M-positive disease foll ...
Leto:
2019
Vir:
Digitalni repozitorij raziskovalnih organizacij Slovenije
Maximilian J Hochmair,
Alessandro Morabito,
Desiree Hao,
Cheng-Ta Yang,
Ross A Soo,
James C-H Yang,
Rasim Gucalp,
Balazs Halmos,
Lara Wang,
Amanda Golembesky,
Angela Märten,
Tanja Čufer
Izvirni znanstveni članek
Oznake:
non-small-cell lung cancer;afatinib;osimertinib;epidermal growth factor receptor;EGFR;observational study;
Aim: To assess outcomes in patients with EGFR mutation-positive (Del19, L858R) non-small-cell lung cancer receiving sequential afatinib and osimertinib in a real-world clinical setting. Materials & methods: In this retrospective, observational, multicenter study, patients (n = 204) had T790M-positiv ...
Leto:
2018
Vir:
Digitalni repozitorij raziskovalnih organizacij Slovenije
Izvirni znanstveni članek
Oznake:
kromosomi;poškodbe;prelomi;radiacija;simulacija;metode;
Leto:
2008
Vir:
dLib.si Digitalna knjižnica Slovenije
Video in druga učna gradiva
Oznake:
computer science;computational linguistics
This paper presents an interdisciplinary investigation of statistical information retrieval (IR) techniques for protein identification from tandem mass spectra, a challenging problem in proteomic data analysis. We formulate the task as an IR problem, by constructing a “query vector” whose elements a ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;on-line learning
In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approximate by an ellipsoid the classification hypotheses that are consistent with all the training examples received so far. This is in contrast to most onlin ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;pattern recognition
Sensors provide computer systems with a window to the outside world. Activity recognition "sees" what is in the window to predict the locations, trajectories, actions, goals and plans of humans and objects. Building an activity recognition system requires a full range of interaction from statistical ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;semantic web;rdf - resource description framework
Data quality issues arise in the Semantic Web because data
is created by diverse people and/or automated tools. In particular, erroneous
triples may occur due to factual errors in the original data source,
the acquisition tools employed, misuse of ontologies, or errors in ontology
alignment. We prop ...
Leto:
2011
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;active learning
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling effo ...
Leto:
2009
Vir:
videolectures.net