Results 211 to 220 of about 96,228 (258)

Evaluation of Dried Plasma Spot‐Based Quantification of Glial Fibrillary Acidic Protein as a Disease‐Associated Biomarker in Neuromyelitis Optica Spectrum Disorder

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate the diagnostic accuracy of glial fibrillary acidic protein (GFAP) measured in dried plasma spots versus conventional plasma‐ and serum‐GFAP testing for assessment of disease severity in aquaporin‐4 immunoglobulin G–positive neuromyelitis optica spectrum disorder (AQP4‐IgG+ NMOSD).
Felix Wohlrab, Evelyn Alvarez, Roua Hamdi, Bingqing Zhang, Hayeun Ji, Niyati Jhaveri, Patrick Schindler, Pedro Sanchez, Pia S. Sperber, Tanja Schmitz‐Hübsch, Frederike C. Oertel, Nisa Vorasoot, Najib Kissani, Falko Böhringer, Jens Kuhle, Anne‐Katrin Pröbstel, Xiao‐Jun Ma, Péter Körtvelyessy, Patrick Waters, Friedemann Paul   +19 more
wiley   +1 more source

When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery. [PDF]

Brief Bioinform
Njume CM, Petracci I, Bellini S, Goljanek-Whysall K, Quinlan LR, Fiszer A, Borroni B, Ghidoni R, Kumbasar A, Cakmak A.   +9 more
europepmc   +1 more source

DeepSentRec: a deep learning-based sentiment-aware product recommendation system. [PDF]

Sci Rep
Kollu SRP, Garapati Y.
europepmc   +1 more source

Efficient talent identification in women's football: A ranking-based approach for goal scoring analysis. [PDF]

PLoS One
Song S, Kim HS.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Feature selection for ranking

Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng, Tie-Yan Liu, Tao Qin 0001, Hang Li 0001   +3 more
openaire   +1 more source

Speeding up Document Ranking with Rank-based Features

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015
Learning to Rank (LtR) is an effective machine learning me- thodology for inducing high-quality document ranking func- tions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vec- tors, a machine-learned function is used to reorder this set.
Lucchese C, Nardini F M, Orlando S, Perego R, Tonellotto N   +4 more
openaire   +2 more sources