Results 101 to 110 of about 25,654,881 (342)
Survey of Multi-task Recommendation Algorithms [PDF]
Single-task recommendation algorithms have problems such as sparse data, cold start and unstable recommendation effect. Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information, to better ...
WEN Minwei, MEI Hongyan, YUAN Fengyuan, ZHANG Xiaoyu, ZHANG Xing
doaj +1 more source
This study reveals how the mitochondrial protein Slm35 is regulated in Saccharomyces cerevisiae. The authors identify stress‐responsive DNA elements and two upstream open reading frames (uORFs) in the 5′ untranslated region of SLM35. One uORF restricts translation, and its mutation increases Slm35 protein levels and mitophagy.
Hernán Romo‐Casanueva +5 more
wiley +1 more source
Cell wall target fragment discovery using a low‐cost, minimal fragment library
LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
Big Data and Recommendation System
In big data era,recommendation system is the key means to tackle the issue of “information overload”.Recommendation system has been widely applied to many domains.The most typical and promising domain is the e-commence.Recently,with the rapid development
Cuiping Li +4 more
doaj
PARP inhibitors are used to treat a small subset of prostate cancer patients. These studies reveal that PARP1 activity and expression are different between European American and African American prostate cancer tissue samples. Additionally, different PARP inhibitors cause unique and overlapping transcriptional changes, notably, p53 pathway upregulation.
Moriah L. Cunningham +21 more
wiley +1 more source
Trends In Tourism Recommendation Systems: A Review
Tourism Recommendation Systems (TRS) are increasingly important in the tourism industry to provide personalized recommendations based on diverse tourist preferences. Technology and big data have transformed TRS from traditional travel agencies to modern
Aderline Song Ke Xin +2 more
doaj
Course Recommendation System based on Natural Language Processing
Smrithi Sumesh, Prof. Aswini S H
openalex +2 more sources
Bridging the gap: Multi‐stakeholder perspectives of molecular diagnostics in oncology
Although molecular diagnostics is transforming cancer care, implementing novel technologies remains challenging. This study identifies unmet needs and technology requirements through a two‐step stakeholder involvement. Liquid biopsies for monitoring applications and predictive biomarker testing emerge as key unmet needs. Technology requirements vary by
Jorine Arnouts +8 more
wiley +1 more source

