Results 201 to 210 of about 671,529 (252)
Effects of Sampling Frequency on Human Activity Recognition with Machine Learning Aiming at Clinical Applications. [PDF]
Yamane T, Kimura M, Morita M.
europepmc +1 more source
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
Metaheuristic-Driven Feature Selection for Human Activity Recognition on KU-HAR Dataset Using XGBoost Classifier. [PDF]
Sarker P, Tiang JJ, Nahid AA.
europepmc +1 more source
This review summarizes the transcription factors, repressive chromatin‐modifying complexes, and epigenetic mechanisms that control fetal hemoglobin repression. Notably, many regulators of γ‐globin silencing also function in transcriptional and epigenetic networks that drive cancer, highlighting opportunities to translate advances in hemoglobinopathy ...
Meigen Yu +3 more
wiley +1 more source
Applying MLP-Mixer and gMLP to Human Activity Recognition. [PDF]
Miyoshi T, Koshino M, Nambo H.
europepmc +1 more source
Interferon type 1 (IFN‐1) production and signaling is associated with the acquisition of therapy resistance, following chronic DNA damage, via Interferon‐related DNA damage resistance signature (IRDS) gene expression. An alternative, DNA damage‐independent role of sustained IFN‐1 mediated resistance was identified and characterized by the emergence of ...
Ashlyn Conant +11 more
wiley +1 more source
A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living. [PDF]
Al Farid F +5 more
europepmc +1 more source
CEACAM1 participation in breast cancer progression
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin +3 more
wiley +1 more source
Wearable Spine Tracker vs. Video-Based Pose Estimation for Human Activity Recognition. [PDF]
Walkling J +3 more
europepmc +1 more source
Dual Attention-Based recurrent neural network and Two-Tier optimization algorithm for human activity recognition in individuals with disabilities. [PDF]
Alkahtani HK, Mohammed GP, Marzouk R.
europepmc +1 more source

