Results 71 to 80 of about 406,491 (258)

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 more
wiley   +1 more source

Circuit Aware Approximate System Design With Case Studies in Image Processing and Neural Networks

IEEE Access, 2019
This paper aims to exploit approximate computing units in image processing systems and artificial neural networks. For this purpose, a general design methodology is introduced, and approximation-oriented architectures are developed for different ...
Tuba Ayhan, Mustafa Altun
doaj   +1 more source

Differential image motion in the short exposure regime

, 2011
Whole atmosphere seeing \beta_0 is the most important parameter in site testing measurements. Estimation of the seeing from a variance of differential image motion is always biased by a non-zero DIMM exposure, which results in a wind smoothing.
B. Safonov, Conan, Fried, Fried, Gradstein, Kellerer, Kornilov, Kornilov, Kornilov, Kornilov, Kornilov, Martin, Sarazin, Sarazin, Soules, Tokovinin, Tokovinin, Tokovinin, V. Kornilov, Vernin   +19 more
core   +1 more source

Crucial parameters for precise copy number variation detection in formalin‐fixed paraffin‐embedded solid cancer samples

Molecular Oncology, EarlyView.
This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris, Vasiliki Siozopoulou, Léon C van Kempen, Anne Sieben, Ella Roelant, Stig Hellemans, Elyne Backx, Laure Sorber, Koen De Winne, Senada Koljenović, Karen Zwaenepoel   +10 more
wiley   +1 more source

Kernel-blending connection approximated by a neural network for image classification

Computational Visual Media, 2020
This paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature
Xinxin Liu, Yunfeng Zhang, Fangxun Bao, Kai Shao, Ziyi Sun, Caiming Zhang   +5 more
doaj   +1 more source

Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

, 2017
Many localization algorithms use a spatiotemporal window of sensory information in order to recognize spatial locations, and the length of this window is often a sensitive parameter that must be tuned to the specifics of the application.
Bruce, Jake, Jacobson, Adam, Milford, Michael   +2 more
core   +1 more source

Tumor mutational burden as a determinant of metastatic dissemination patterns

Molecular Oncology, EarlyView.
This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal, Andrea Moreno‐Manuel, Miguel Salvadó‐Pertierra, Cristina Santos‐Vivas, Rebeca Sanz‐Pamplona   +4 more
wiley   +1 more source

Energy efficient design and implementation of approximate adder for image processing applications [PDF]

Serbian Journal of Electrical Engineering
Approximate computing is a new technique that promises to speed up computations while preserving a level of precision suitable for error-tolerant systems such as neural networks and graphics. At the edge, a lot of computationally complex methods
Naik Jatothu Brahmaiah, Kumar Kanagala Sateesh, Krishnaiah Kondragunta Rama, Koteswararao Seelam   +3 more
doaj   +1 more source

Inference by Minimizing Size, Divergence, or their Sum

, 2012
We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL
McCallum, Andrew, Riedel, Sebastian, Smith, David A.   +2 more
core   +3 more sources

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source