Results 11 to 20 of about 118,355 (211)

Offline Multi-Objective Optimization

CoRR
Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expensive to evaluate, numerous complex real-world problems entail optimizing multiple conflicting objectives, i.e., multi-objective optimization (MOO). Nevertheless, offline MOO
Ke Xue 0001, Rong-Xi Tan, Xiaobin Huang, Chao Qian 0001   +3 more
openaire   +3 more sources

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

Keratin 19 as a prognostic marker and contributing factor of metastasis and chemoresistance in high‐grade serous ovarian cancer

Molecular Oncology, EarlyView.
Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch, Isabel Vogel, Sina Nokodian, Johanna Moeller, Antonia Blechschmidt, Vicky Hecht, Vanessa Kuentzel, Katharina Thiedig, Melissa Schwab, Oliver Schilling, Holger Bronger, Marion Kiechle, Viktor Magdolen, Tobias Dreyer   +13 more
wiley   +1 more source

In-Context Multi-Objective Optimization

CoRR
Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such expensive, black-box problems: it fits probabilistic surrogates and selects new designs via an acquisition function that balances exploration and exploitation.
Xinyu Zhang, Conor Hassan, Julien Martinelli, Daolang Huang, Samuel Kaski   +4 more
openaire   +2 more sources

A Multi-Objective Interpretation of Optimal Transport [PDF]

Journal of Optimization Theory and Applications, 2017
This paper connects discrete optimal transport to a certain class of multi-objective optimization problems. In both settings, the decision variables can be organized into a matrix. In the multi-objective problem, the notion of Pareto efficiency is defined in terms of the objectives together with non-negativity constraints and with equality constraints ...
openaire   +5 more sources

Longitudinal circulating tumor DNA profiling in patients with advanced endometrial cancer using an off‐the‐shelf targeted NGS panel

Molecular Oncology, EarlyView.
Intratumour heterogeneity complicates precision management of advanced endometrial cancer. Circulating tumor DNA (ctDNA) offers a minimally invasive strategy to capture tumor evolution and therapeutic resistance. Here, we compare tumor‐agnostic NGS with tumor‐informed ddPCR, outlining their relative sensitivity, concordance, and clinical implications ...
Carlos Casas‐Arozamena, Karin Teien Lande, Eva Diaz, Ana Vilar, Juan Cueva, Efigenia Arias, Victoria Sampayo, Alicia Abalo, Nerea González, Eva Colás, Antonio Gil‐Moreno, Miguel Abal, Gema Moreno‐Bueno, Therese Sørlie, Kristina Lindemann, Laura Muinelo‐Romay   +15 more
wiley   +1 more source

The Multi-Objective Polynomial Optimization

Mathematics of Operations Research
The multi-objective optimization is to optimize several objective functions over a common feasible set. Because the objectives usually do not share a common optimizer, people often consider (weakly) Pareto points. This paper studies multi-objective optimization problems that are given by polynomial functions.
Jiawang Nie, Zi Yang
openaire   +2 more sources

Multi-objective Flower Algorithm for Optimization

Procedia Computer Science, 2013
Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective flower algorithm can accurately ...
Xin-She Yang 0001, Mehmet Karamanoglu, Xingshi He   +2 more
openaire   +2 more sources

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source

Aligned Multi Objective Optimization

CoRR
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machine learning practice, there are many scenarios where such conflict does not take place.
Efroni, Yonathan, Kretzu, Ben, Jiang, Daniel, Bhandari, Jalaj, Zheqing, Zhu, Ullrich, Karen   +6 more
openaire   +3 more sources