Results 21 to 30 of about 2,965,444 (278)
Gene function prediction using labeled and unlabeled data
BMC Bioinformatics, 2008 Background In general, gene function prediction can be formalized as a classification problem based on machine learning technique. Usually, both labeled positive and negative samples are needed to train the classifier.
Wang Yong +3 more
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Automated Quantization and Retraining for Neural Network Models Without Labeled Data
IEEE Access, 2022 Deploying neural network models to edge devices is becoming increasingly popular because such deployment decreases the response time and ensures better data privacy of services.
Kundjanasith Thonglek +7 more
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Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets [PDF]
, 2019 Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training.
Belar, Oihana +7 more
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Multi-Label Ranking: Mining Multi-Label and Label Ranking Data
, 2023 We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art
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Census 2: isobaric labeling data analysis [PDF]
Bioinformatics, 2014 Abstract Motivation: We introduce Census 2, an update of a mass spectrometry data analysis tool for peptide/protein quantification. New features for analysis of isobaric labeling, such as Tandem Mass Tag (TMT) or Isobaric Tags for Relative and Absolute Quantification (iTRAQ), have been added in this version, including a reporter ion ...
Sung Kyu Robin, Park +11 more
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Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
, 2020 This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors ...
Katsuki, Takayuki +2 more
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Semi-Supervised Sparse Coding [PDF]
, 2014 Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised ...
Gao, Xin, Wang, Jim Jing-Yan
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Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data. [PDF]
PLoS ONE, 2013 In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance.
Jian Cui +7 more
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Iterative Multiplicative Filters for Data Labeling [PDF]
International Journal of Computer Vision, 2017 Based on an idea in [4] we propose a new iterative multiplicative filtering algorithm for label assignment matrices which can be used for the supervised partitioning of data. Starting with a row-normalized matrix containing the averaged distances between prior features and the observed ones the method assigns in a very efficient way labels to the data.
Ronny Bergmann +3 more
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Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from ...
Li, Rumeng, Wang, Xun, Yu, Hong
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