Results 71 to 80 of about 5,782,720 (331)

Feeding, Fear, and Acceptance: Caregivers’ Experiences of Nutritional Support During Childhood Cancer Treatment

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose To explore caregivers' experiences and perceptions of the nutritional challenges faced by children and adolescents undergoing cancer treatment, including oral intake and the use of nutritional support. Methods This was a qualitative study based on focus groups with parents and caregivers at a Brazilian pediatric oncology hospital ...
Danyelle Apparecida Balsamão Marques, Mariana dos Santos Murra, Welinton Hirai, Ana Laura Balduino do Nascimento, Karla Emilia de Sá Rodrigues, Wilson Elias de Oliveira Junior   +5 more
wiley   +1 more source

On the Importance of Train-Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Remote Sensing, 2020
Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning
K. Pawłuszek-Filipiak, A. Borkowski
semanticscholar   +1 more source

A semi-supervised spam mail detector [PDF]

, 2006
This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge.
Pfahringer, Bernhard
core  

Organoids in pediatric cancer research

FEBS Letters, EarlyView.
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley   +1 more source

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source

Supervised classification and mathematical optimization [PDF]

Computers & Operations Research, 2013
Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized.
Emilio Carrizosa, Dolores Romero Morales
openaire   +4 more sources

Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints [PDF]

, 2021
Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types
Akshansh, Mishra
core   +1 more source

Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry

FEBS Letters, EarlyView.
EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri, Ayaka Nagasato‐Ichikawa, Keita Iida, Shuhei Kimura, Akira Imamoto, Mariko Okada   +5 more
wiley   +1 more source

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

IEEE Access
Malware classification with supervised learning requires a large dataset, which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification.
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

Metastasis on pause: How dormant tumor cells stay hidden within the tumor microenvironment and evade immune surveillance

Molecular Oncology, EarlyView.
Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary, Jose Javier Bravo‐Cordero   +1 more
wiley   +1 more source