Results 91 to 100 of about 762,608 (309)
Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
wiley +1 more source
, 2016 Existing fine-grained visual categorization methods often suffer from three challenges: lack of training data, large number of fine-grained categories, and high intraclass vs. low inter-class variance.
Belongie, Serge +3 more
core +1 more source
Constraint selection in metric learning
Knowledge-Based Systems, 2018 A number of machine learning algorithms are using a metric, or a distance, in order to compare individuals. The Euclidean distance is usually employed, but it may be more efficient to learn a parametric distance such as Mahalanobis metric. Learning such a metric is a hot topic since more than ten years now, and a number of methods have been proposed to
openaire +3 more sources
, 2019 Recent literature has shown that symbolic data, such as text and graphs, is often better represented by points on a curved manifold, rather than in Euclidean space. However, geometrical operations on manifolds are generally more complicated than in Euclidean space, and thus many techniques for processing and analysis taken for granted in Euclidean ...
Aalto, Max, Verma, Nakul
openaire +2 more sources
Molecular Oncology, EarlyView.Proteomic and phosphoproteomic analyses were performed on lung adenocarcinoma (LUAD) tumors with EGFR, KRAS, or EML4–ALK alterations and wild‐type cases. Distinct protein expression and phosphorylation patterns were identified, especially in EGFR‐mutated tumors. Key altered pathways included vesicle transport and RNA splicing.
Fanni Bugyi +12 more
wiley +1 more source
No Fuss Distance Metric Learning using Proxies
, 2017 We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal ...
Ioffe, Sergey +4 more
core +1 more source
Decrypting cancer's spatial code: from single cells to tissue niches
Molecular Oncology, EarlyView.Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik +4 more
wiley +1 more source
Molecular Oncology, EarlyView.We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin +10 more
wiley +1 more source
FEBS Open Bio, EarlyView.The tumor microenvironment is a dynamic, multifaceted complex system of interdependent cellular, biochemical, and biophysical components. Three‐dimensional in vitro models of the tumor microenvironment enable a better understanding of these interactions and their impact on cancer progression and therapeutic resistance.
Salma T. Rafik +3 more
wiley +1 more source
A Survey on Metric Learning for Feature Vectors and Structured Data
, 2013 The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult.
Bellet, Aurélien +2 more
core