Results 101 to 110 of about 19,696,345 (257)

Crossfire: cross-domain feature integration for robust time series classification [PDF]

PeerJ Computer Science
Feature-based time series classification (TSC) methods have traditionally relied on time-domain features, which can limit their effectiveness in capturing the full spectrum of temporal dynamics.
Celal Alagöz
doaj   +2 more sources

Time-series clustering via quasi U-statistics

, 2015
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The problem of time-series discrimination and classification is discussed. We propose a novel clustering algorithm based on a
Pinheiro, A, Valk, M
core   +1 more source

A Benchmark for Early Time-Series Classification (Extended Abstract)

, 2023
The objective of Early Time-Series Classification (ETSC) is to predict the class of incoming time-series by observing the fewest time-points possible.
Kamberi, Petro-Foti, Kladis, Evgenios, Akasiadis, Charilaos   +2 more
core   +1 more source

Characterization of therapeutic antibodies targeting lymphatic and vascular resistance factor for the treatment of hypervascularized and highly metastatic cancers

Molecular Oncology, EarlyView.
LVRF (lymphatic and vascular resistance factor) is a VEGFA splice variant upregulated in response to anti‐angiogenic therapy that fosters tumor adaptation through enhanced vascularization, lymphangiogenesis, and metastatic dissemination. Targeting LVRF with fully human neutralizing antibodies disrupts these processes and limits resistant tumor growth ...
Marion David, Arthur Karaulic, Audrey Bennetot, Clémence Fournier, Quentin Odet, Arthur Gouraud, Isabelle Bourget, Cédric Gaggioli, Sandy Giuliano, Frédéric Luciano, Olivier Léger, Zaki Sellam, Gilles Pagès   +12 more
wiley   +1 more source

Reprogramming tumor cells with LCOR mRNA nanotherapy to enhance immunotherapy

Molecular Oncology, EarlyView.
LCOR (Ligand‐dependent corepressor) mRNA encapsulated in polymeric nanoparticles restores LCOR activity in tumor cells, constraining aggressive tumor cell states and increasing tumor immunogenicity. This reprogramming makes tumor cells more vulnerable to T‐cell attack and enhances their response to immune‐checkpoint immunotherapy.
Gabriel Serra‐Mir, Elena Haro‐Martínez, Sandra Blasco‐Benito, Chiara Cannatà, José Ángel Palomeque, Anna López‐Plana, Maria Sanz‐Flores, Marc Sabartés, Cristina Fornaguera, Toni Celià‐Terrassa   +9 more
wiley   +1 more source

Multidimensional time series classification with multiple attention mechanism

Complex & Intelligent Systems
The classification of multidimensional time series holds significant importance across various domains, including action classification, medical diagnosis, and credit assessment.
Chen Liu, Zihan Wei, Lixin Zhou, Ying Shao   +3 more
doaj   +1 more source

Self-Supervised Learning-Based Time Series Classification via Hierarchical Sparse Convolutional Masked-Autoencoder

IEEE Open Journal of Signal Processing
In recent years, the use of time series analysis has become widespread, prompting researchers to explore methods to improve classification. Time series self-supervised learning has emerged as a significant area of study, aiming to uncover patterns in ...
Ting Yu, Kele Xu, Xu Wang, Bo Ding, Dawei Feng   +4 more
doaj   +1 more source

Spatial heterogeneity of hotspot mutations and clonal relatedness in ovarian endometrioid carcinoma

Molecular Oncology, EarlyView.
Spatially resolved analysis revealed heterogeneous distributions of selected cancer‐associated mutations within ovarian endometrioid carcinomas. Shared mutations between paired endometrial and ovarian tumors supported clonal relatedness. These findings highlight the value of sampling multiple tumor regions when interpreting molecular profiles ...
Takahito Ashihara, Kosuke Murakami, Shiro Takamatsu, Akiko Kanto, Seiichi Mori, Tomoyuki Otani, Kazuko Sakai, Yasushi Kotani, Kazuto Nishio, Kaoru Abiko, Noriomi Matsumura   +10 more
wiley   +1 more source

Bayesian time series classification.

, 2016
This paper proposes an approach to classification of adjacent segments of a time series as being either of classes. We use a hierarchical model that consists of a feature extraction stage and a generative classifier which is built on top of these features. Such two stage approaches are often used in signal and image processing.
Sykacek, P, Roberts, S
openaire   +2 more sources

Classification of non‐stationary time series

Stat, 2014
AbstractIn this paper we consider the problem of classifying non‐stationary time series. The method that we introduce is based on the locally stationary wavelet paradigm and seeks to take account of the fact that there may be within‐class variation in the signals being analysed.
Krzemieniewska, Karolina, Eckley, Idris, Fearnhead, Paul   +2 more
openaire   +2 more sources