Results 41 to 50 of about 13,577 (310)

A New Method for Solving Supervised Data Classification Problems

Abstract and Applied Analysis, 2014
Supervised data classification is one of the techniques used to extract nontrivial information from data. Classification is a widely used technique in various fields, including data mining, industry, medicine, science, and law. This paper considers a new
Parvaneh Shabanzadeh, Rubiyah Yusof
doaj   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

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, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gábor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Patient therapy outcome modeling in cancer organoids is improved by cancer‐associated fibroblasts and organoid assembly convolution

Molecular Oncology, EarlyView.
Patient‐derived organoids (PDOs) from pancreatic, colorectal, and gastric cancers were used to evaluate standard and experimental therapies. Incorporating cancer‐associated fibroblasts (CAFs) into organoid cultures improved patient therapy outcome prediction.
Marcin Grochowski, Liudmyla Dolinchuk, Michał Jerzak, Albert Gandurski, Tomasz Grochowski, Weronika Wojtyś, Maciej Zadrożny, Wojciech Kaźmierczak, Małgorzata Lenarcik, Marta Matejak‐Górska, Radosław Samsel, Tomasz Olesiński, Dawid Walerych   +12 more
wiley   +1 more source

Two Stage Stochastic Programing Based on the Sample Average Approximation and Accelerated Benders Decomposition for Designing Closed-loop Supply Chain Network Design under Uncertainty [PDF]

مجله مدل سازی در مهندسی, 2017
In this paper, a comprehensive mathematical model for designing supply chain network via considering integrated flow of forward and reverse of multiple products during multiple periods is proposed.
Aliakbar Hasani
doaj   +1 more source

A COMPARATIVE STUDY ON OPTIMIZATION METHODS FOR THE CONSTRAINED NONLINEAR PROGRAMMING PROBLEMS [PDF]

, 2004
Constrained nonlinear programming problems often arise in many engineering appli-cations. The most well-known optimization methods for solving these problems are se-quential quadratic programming methods and generalized reduced gradient methods.
Ozgur Yeniay, Yeniay, O
core   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Adaptive Dynamic Programming with Reinforcement Learning on Optimization of Flight Departure Scheduling

Aerospace
The intricacies of air traffic departure scheduling, especially when numerous flights are delayed, frequently impede the implementation of automated decision-making for scheduling.
Hong Liu, Song Li, Fang Sun, Wei Fan, Wai-Hung Ip, Kai-Leung Yung   +5 more
doaj   +1 more source

Mathematical Modelling of Robust Optimization for Integer Programming Problem

Journal of Institute of Science and Technology, 2015
Dealing with data uncertainty in mathematical programming has been recognized as a central problem in optimization for a long time. There are two methods that have been proposed to address data uncertainty over the years, namely stochastic programming and robust optimization.
openaire   +2 more sources

A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems

International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.
A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam, Anthony Siming Chen, Guido Herrmann   +2 more
wiley   +1 more source