Results 41 to 50 of about 108,016 (310)
The complexity of quantum support vector machines [PDF]
QuantumQuantum support vector machines employ quantum circuits to define the kernel function. It has been shown that this approach offers a provable exponential speedup compared to any known classical algorithm for certain data sets. The training of such models
Gian Gentinetta +3 more
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The ubiquitin ligase RNF115 is required for the clearance of damaged lysosomes
FEBS Letters, EarlyView.Upon lysosomal rupture, an E3 ubiquitin ligase RNF115 translocates from the cytosol to the damaged lysosomal membrane. Moreover, RNF115 depletion impairs the clearance of damaged lysosomes, identifying it as a key regulator of lysosomal quality control.
Sae Nakanaga +3 more
wiley +1 more source
Modelling Proteolytic Enzymes With Support Vector Machines
Journal of Integrative Bioinformatics, 2011 The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable.
Morgado Lionel +3 more
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pH‐mediated activation of the lysosomal arginine sensor SLC38A9
FEBS Letters, EarlyView.Cells monitor nutrient levels via the lysosomal transporter SLC38A9 to activate the mechanistic target of rapamycin complex 1 (mTORC1). This study reveals that SLC38A9 function is regulated by pH. We identified histidine 544 as a critical pH sensor that undergoes conformational changes to control amino acid efflux from lysosomes; therefore, it ...
Xuelang Mu, Ampon Sae Her, Tamir Gonen
wiley +1 more source
The human gut microbiome across the life course
FEBS Letters, EarlyView.Despite significant individual variation and continuous change throughout life, the human gut microbiome follows some life stage‐specific trends. This article provides a brief overview of how gut microbiome composition shifts across different phases of life. Created in BioRender. Özkurt, E. (2026) https://BioRender.com/8q4nrnc.
Alise J. Ponsero +4 more
wiley +1 more source
Minimal Support Vector Machine
CoRR, 2018 Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes. This hyperplane is determined by support vectors. In existing SVM formulations, the objective function uses L2 norm or L1 norm on slack variables. The number of support vectors is a measure of generalization errors. In
Shuai Zheng 0002, Chris Ding
openaire +2 more sources
RSVM: Reduced Support Vector Machines
, 2001 An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation.
Olvi L. Mangasarian +3 more
core +1 more source
Molecular Oncology, EarlyView.Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson +9 more
wiley +1 more source
Minimal Complexity Support Vector Machines for Pattern Classification
Computers, 2020 Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique.
Shigeo Abe
doaj +1 more source
Extending twin support vector machine classifier for multi-category classification problems [PDF]
, 2013 © 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems.
Liu, X +5 more
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