Results 161 to 170 of about 707,608 (244)
The role of online news sentiment in carbon price prediction of China's carbon markets. [PDF]
Liu M, Ying Q.
europepmc +1 more source
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris +10 more
wiley +1 more source
Price prediction of PFP NFT based on the sentiments of users in posts on social media. [PDF]
Jang S, Lee D.
europepmc +1 more source
Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm. [PDF]
Feng M, Duan Y, Wang X, Zhang J, Ma L.
europepmc +1 more source
Single circulating tumor cells (sCTCs) from high‐grade serous ovarian cancer patients were enriched, imaged, and genomically profiled using WGA and NGS at different time points during treatment. sCTCs revealed enrichment of alterations in Chromosomes 2, 7, and 12 as well as persistent or emerging oncogenic CNAs, supporting sCTC identity.
Carolin Salmon +9 more
wiley +1 more source
Correction: Carbon price prediction based on a scaled PCA approach. [PDF]
PLOS One Staff.
europepmc +1 more source
A comparative study on effect of news sentiment on stock price prediction with deep learning architecture. [PDF]
Dahal KR +7 more
europepmc +1 more source
Aptamers are used both therapeutically and as targeting agents in cancer treatment. We developed an aptamer‐targeted PLGA–TRAIL nanosystem that exhibited superior therapeutic efficacy in NOD/SCID breast cancer models. This nanosystem represents a novel biotechnological drug candidate for suppressing resistance development in breast cancer.
Gulen Melike Demirbolat +8 more
wiley +1 more source
A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm. [PDF]
Sun K, Yao Q, Li Y.
europepmc +1 more source
Carbon price prediction based on multi-factor MEEMD-LSTM model. [PDF]
Min Y, Shuzhen Z, Wuwei L.
europepmc +1 more source

