Results 91 to 100 of about 1,101,099 (365)

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Automatic Morpheme-based Distractors Generation for Fill-in-the-Blank Questions using Listwise Learning-To-Rank Method for Agglutinative Language

Engineering Science and Technology, an International Journal, 2022
Automatic question generation facilitates the smart assessment for the evaluator to assess the student skills. Several methods were proposed to generate distractors for non-factoid cloze question using different similarity measures. This study presents a
Shanthi Murugan, Balasundaram Sadhu Ramakrishnan   +1 more
doaj   +1 more source

Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data

Molecular Oncology, EarlyView.
This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.
Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda   +12 more
wiley   +1 more source

Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval

Iranian Journal of Information Processing & Management, 2021
Ranking algorithms, as the core of web search systems, are responsible for finding and ranking the most relevant documents to user information needs from the crawled and indexed corpus.
Mohaddeseh Mahjoob, Faezeh Ensan, Sanaz Keshvari, Parastoo Jafarzadeh, Mohammadamin keyvanzad   +4 more
doaj  

Regression and Learning to Rank Aggregation for User Engagement Evaluation

, 2014
User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as ...
Moradi, Pooya, Shakery, Azadeh, Zamani, Hamed   +2 more
core   +1 more source

Learning to rank on graphs [PDF]

Machine Learning, 2010
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applications, and many others. There has been much work in recent years on developing learning algorithms for such graph data; in particular, graph learning algorithms have been ...
openaire   +2 more sources

Learning to Rank Retargeted Images [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Image retargeting techniques that adjust images into different\ud sizes have attracted much attention recently. Objective\ud quality assessment (OQA) of image retargeting results\ud is often desired to automatically select the best results. Existing\ud OQA methods output an absolute score for each retargeted\ud image and use these scores to compare ...
Yang Chen, Yong-Jin Liu, Yu-Kun Lai
openaire   +3 more sources

Data‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Molecular Oncology, EarlyView.
This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo   +8 more
wiley   +1 more source

Learning to Rank System Configurations [PDF]

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
Information Retrieval (IR) systems heavily rely on a large number of parameters, such as the retrieval model or various query expansion parameters, whose values greatly influence the overall retrieval effectiveness. However, setting all these parameters individually can often be a tedious task, since they can all affect one another, while also vary for
Deveaud, Romain, Mothe, Josiane, Nie, Jian-Yun   +2 more
openaire   +2 more sources

Circulating tumor DNA monitoring and blood tumor mutational burden in patients with metastatic solid tumors treated with atezolizumab

Molecular Oncology, EarlyView.
In patients treated with atezolizumab as a part of the MyPathway (NCT02091141) trial, pre‐treatment ctDNA tumor fraction at high levels was associated with poor outcomes (radiographic response, progression‐free survival, and overall survival) but better sensitivity for blood tumor mutational burden (bTMB).
Charles Swanton, Russell W. Madison, Candice Francheska B. Tambaoan, Funda Meric‐Bernstam, Christopher J. Sweeney, Razelle Kurzrock, Howard A. Burris III, David R. Spigel, Hanna Tukachinsky, Jason Hughes, Julia Malato, Bongin Yoo, Tania Szado, Cheryl Schwab, Lincoln W. Pasquina, Amaya Gasco, Katja Schulze, Claire F. Friedman   +17 more
wiley   +1 more source