Results 61 to 70 of about 74,247 (264)

A yeast model of 5‐oxoproline accumulation reveals a general toleration to 5‐oxoproline

FEBS Open Bio, EarlyView.
Using a yeast model, we show that even high accumulation of 5‐oxoproline causes only mild cellular stress and does not trigger oxidative stress. Instead, cells adapt by activating efflux pumps and diverse protective pathways, suggesting that previously proposed harmful effects of 5‐oxoproline may arise from indirect metabolic imbalances rather than the
Pratiksha Dubey, Vaishnavi Sanjayan, Praveen Singh, Shantanu Sengupta, Anand Kumar Bachhawat   +4 more
wiley   +1 more source

Transmembrane protein topology prediction using support vector machines

BMC Bioinformatics, 2009
Background Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are
Nugent Timothy, Jones David T
doaj   +1 more source

Versatile vector tools for efficient protein screening across multiple expression systems

FEBS Open Bio, EarlyView.
A unified vector toolkit enables rapid protein expression screening across E. coli, insect, and mammalian cells. A single primer pair amplifies the target gene, which is inserted into any vector via a standardized interface. This streamlined workflow eliminates repeated cloning steps, accelerating the identification of optimal expression conditions for
Zhimin Zhu, Yaqing Liu, Lei Qin, Feng Yu, Qisheng Wang, Yahui Liu   +5 more
wiley   +1 more source

Analysis of support vector machines [PDF]

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2003
We compare L1 and L2 soft margin support vector machines from the standpoint of positive definiteness, the number of support vectors, and uniqueness and degeneracy of solutions. Since the Hessian matrix of L2 SVM is positive definite, the number of support vectors for L2 SVM is larger than or equal to the number of L1 SVM.
openaire   +1 more source

Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells

FEBS Open Bio, EarlyView.
Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim, Marco Corazzari, Iman Osman, Jane Armstrong, Noel Carter   +4 more
wiley   +1 more source

Support vector machines for optimal channel decoding

EURASIP Journal on Wireless Communications and Networking
In this work, we investigate channel decoding techniques based on machine learning, and more specifically, on support vector machines (SVMs). Existing SVM-based decoders suffer from a scalability problem, characterized by the exponential growth of both ...
Gastón De Boni Rovella, Meryem Benammar, Tarik Benaddi, Hugo Méric   +3 more
doaj   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

Aplikasi Metode Cross Entropy untuk Support Vector Machines

Jurnal Teknik Industri, 2012
Support vector machines (SVM) is a robust method for  classification problem. In the original formulation, the dual form of SVM must be solved by a quadratic programming in order to get the optimal solution.
Budi Santosa, Tiananda Widyarini
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Advances in Civil Engineering, 2018
Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses.
Yang Sun, Xianda Feng, Lingqiang Yang
doaj   +1 more source