Results 81 to 90 of about 11,467,392 (329)

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Deep active learning for classifying cancer pathology reports

BMC Bioinformatics, 2021
Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive.
Kevin De Angeli, Shang Gao, Mohammed Alawad, Hong-Jun Yoon, Noah Schaefferkoetter, Xiao-Cheng Wu, Eric B. Durbin, Jennifer Doherty, Antoinette Stroup, Linda Coyle, Lynne Penberthy, Georgia Tourassi   +11 more
doaj   +1 more source

Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning. [PDF]

Physical Chemistry, Chemical Physics - PCCP, 2018
We propose a methodology for the fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining ring polymer molecular dynamics (RPMD) and machine-learning interatomic potentials actively learning on-
I. Novikov, Y. V. Suleimanov, A. Shapeev
semanticscholar   +1 more source

Profiling neoadjuvant therapy response in rectal cancer using meta‐analysis of publicly available transcriptomic RNA‐seq datasets

Molecular Oncology, EarlyView.
This study integrates publicly available transcriptomic datasets to identify molecular signatures associated with response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. By analyzing a combination of multiple cohorts with bioinformatics approaches, we reveal biological pathways and immune‐related features that may improve ...
Aleksandra Stanojevic, Rafael Stroggilos, Mladen Marinkovic, Ana Djuric, Suzana Stojanovic‐Rundic, Radmila Jankovic, Sergi Castellvi‐Bel, Remond J.A. Fijneman, Antonia Vlahou, Jerome Zoidakis, Milena Cavic   +10 more
wiley   +1 more source

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning

Science and Technology of Advanced Materials, 2019
Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental datasets in the polymer-science field are typically limited and they are expensive to build. Their size (< 100 samples)
Sirawit Pruksawan, Guillaume Lambard, Sadaki Samitsu, Keitaro Sodeyama, Masanobu Naito   +4 more
doaj   +1 more source

Active Learning for Interactive Neural Machine Translation of Data Streams [PDF]

Conference on Computational Natural Language Learning, 2018
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation. The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a ...
Álvaro Peris, F. Casacuberta
semanticscholar   +1 more source

Gut microbiota alterations in patients with non‐small‐cell lung cancer undergoing chemoradiotherapy

Molecular Oncology, EarlyView.
We investigated the impact of concurrent chemoradiotherapy (CRT) on the gut microbiota in patients with locally advanced non‐small‐cell lung cancer. Overall gut microbiota composition remained largely stable throughout CRT while antibiotic exposure may influence microbial diversity.
Hanne Marte Nymoen, Zhi Zhao, Henrik Horndalsveen, Maria Moksnes Bjaanæs, Bjørn Henning Grønberg, Tarje Halvorsen, Tesfaye Madebo, Marianne Aanerud, Jussi Koivunen, Kersti Oselin, Saulius Cicenas, Nina Helbekkmo, Jarkko Ahvonen, Maria Silvoniemi, Tine Norman Alver, Tonje Dalen, Åsa Kristina Öjlert, Saima Jamil Farooqi, Åslaug Helland, Vilde Drageset Haakensen   +19 more
wiley   +1 more source

Probe microscopy is all you need

Machine Learning: Science and Technology, 2023
We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods.
Sergei V Kalinin, Rama Vasudevan, Yongtao Liu, Ayana Ghosh, Kevin Roccapriore, Maxim Ziatdinov   +5 more
doaj   +1 more source

Classifying Human Activities Using Machine Learning and Deep Learning Techniques

, 2023
Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart Watches, which can help them manage their daily routines in a healthy way.
Satya Uday Sanku, Satti Thanuja Pavani, T. Jaya Lakshmi, Y. V. Nandini   +3 more
openaire   +3 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source