Results 41 to 50 of about 5,527,579 (363)
IEEE Transactions on Evolutionary Computation, 2019 Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed.
Linqiang Pan +5 more
semanticscholar +1 more source
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm
Journal of Applied Mathematics, 2013 A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function ...
Wanxing Sheng +4 more
doaj +1 more source
Molecular Oncology, EarlyView.There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes +20 more
wiley +1 more source
Decomposition-Based Multi-Objective Evolutionary Algorithm Design Under Two Algorithm Frameworks
IEEE Access, 2020 The development of efficient and effective evolutionary multi-objective optimization (EMO) algorithms has been an active research topic in the evolutionary computation community. Over the years, many EMO algorithms have been proposed.
Lie Meng Pang, Hisao Ishibuchi, Ke Shang
doaj +1 more source
Classification Based on Brain Storm Optimization With Feature Selection
IEEE Access, 2021 Classification is one of the most classic problems in machine learning. Due to the global optimization ability, evolutionary computation (EC) techniques have been successfully applied to solve many problems and the evolutionary classification model is ...
Yu Xue, Yan Zhao, Adam Slowik
doaj +1 more source
Molecular Oncology, EarlyView.This study used longitudinal transcriptomics and gene‐pattern classification to uncover patient‐specific mechanisms of chemotherapy resistance in breast cancer. Findings reveal preexisting drug‐tolerant states in primary tumors and diverse gene rewiring patterns across patients, converging on a few dysregulated functional modules. Despite receiving the
Maya Dadiani +14 more
wiley +1 more source
Image Noise Elimination Using Evolutionary Algorithm [PDF]
Engineering and Technology Journal, 2005 Evolutionary algorithms like Genetic Algorithms ( GA ) and Genetic programming ( GP ) are domain - independent problem solving approaches in which computer programs are evolved to solve , or approximately solve , problems .
Matheel Abdulmunim
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Complex System Modeling and Simulation, 2023 Portfolio optimization is a classical and important problem in the field of asset management, which aims to achieve a trade-off between profit and risk.
Feng Wang, Zilu Huang, Shuwen Wang
doaj +1 more source
Molecular Oncology, EarlyView.B‐cell chronic lymphocytic leukemia (B‐CLL) and monoclonal B‐cell lymphocytosis (MBL) show altered proteomes and phosphoproteomes, analyzed using mass spectrometry, protein microarrays, and western blotting. Identifying 2970 proteins and 316 phosphoproteins, including 55 novel phosphopeptides, we reveal BCR and NF‐kβ/STAT3 signaling in disease ...
Paula Díez +17 more
wiley +1 more source