Results 101 to 110 of about 1,080,302 (365)
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 ...
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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 +2 more
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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 +17 more
wiley +1 more source
Molecular Oncology, EarlyView.A DIA‐MS‐based proteomics analysis of serum samples from GB patients and healthy controls showed that high levels of IL1R2 and low levels of CRTAC1 and HRG in serum are associated with poor survival outcomes for GB patients. These circulating proteins could serve as biomarkers for the prediction of outcome in patients with GB.
Anne Clavreul +11 more
wiley +1 more source
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
Mask Optimization for Image Inpainting
IEEE Access, 2018 This paper proposes a novel approach to image inpainting that optimizes the shape of masked regions given by users. In image inpainting, which removes and restores unwanted regions in images, users draw masks to specify the regions. However, it is widely
Mariko Isogawa +4 more
doaj +1 more source
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
, 2017 We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As
Chang Ming-Wei +12 more
core +1 more source
Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
wiley +1 more source
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
, 2017 European PhD Thesis.
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Molecular Oncology, EarlyView.Proteomic and phosphoproteomic analyses were performed on lung adenocarcinoma (LUAD) tumors with EGFR, KRAS, or EML4–ALK alterations and wild‐type cases. Distinct protein expression and phosphorylation patterns were identified, especially in EGFR‐mutated tumors. Key altered pathways included vesicle transport and RNA splicing.
Fanni Bugyi +12 more
wiley +1 more source