Results 51 to 60 of about 219,224 (269)
FEBS Open Bio, EarlyView.In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes +32 more
wiley +1 more source
Unsupervised end-to-end training with a self-defined target
Neuromorphic Computing and EngineeringDesigning algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phases of self-supervised and supervised learning are accurate and adaptable to
Dongshu Liu +4 more
doaj +1 more source
Inductive Supervised Quantum Learning [PDF]
Physical Review Letters, 2017 6+10 ...
Alex Monràs, Gael Sentís, Peter Wittek
openaire +3 more sources
Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system
FEBS Open Bio, EarlyView.An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková +3 more
wiley +1 more source
To Compress or Not to Compress—Self-Supervised Learning and Information Theory: A Review
EntropyDeep neural networks excel in supervised learning tasks but are constrained by the need for extensive labeled data. Self-supervised learning emerges as a promising alternative, allowing models to learn without explicit labels.
Ravid Shwartz Ziv, Yann LeCun
doaj +1 more source
CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW
Iraqi Journal for Computers and Informatics, 2021 Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data.
Aska Ezadeen Mehyadin +1 more
doaj +1 more source
A Modal Logic for Supervised Learning
Journal of Logic, Language and Information, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alexandru Baltag +2 more
openaire +3 more sources
Why human connection is the true metric of research success
FEBS Open Bio, EarlyView.Human‐centred mentorship can be shaped by mentor attributes, actions, intrinsic drive and career ambition. Drawing on reflections across Singapore and France, as well as workshop insights from FEBS‐IUBMB ENABLE 2024, this article shows that human‐centred mentorship creates the conditions for sustainable growth, well‐being and retention in research ...
Timothy Lin Yun Tan +3 more
wiley +1 more source
Machine Learning with ApplicationsDrug discovery and development is a time-consuming process that involves identifying, designing, and testing new drugs to address critical medical needs. In recent years, machine learning (ML) has played a vital role in technological advancements and has
George Obaido +7 more
doaj +1 more source
Pathways and pitfalls: a qualitative study of student experiences in biomedical science education
FEBS Open Bio, EarlyView.Biomedical science students from underrepresented backgrounds face barriers including financial strain, disrupted laboratory access and cultural exclusion. Peer networks provide vital support when institutional systems are difficult to navigate. To create inclusive learning environments and achieve academic success, educators should blend active, hands‐
Olivia J. Russell +8 more
wiley +1 more source