24 papers · ranked by Valyu relevance
Pablo Japón, Jesús Miró-Bueno, Ángel Goñi-Moreno
Gene expression transforms information encoded in DNA into functional protein activity. Although these systems are often assumed to be static-thus enabling the programming of genetic circuits to meet predefined specifications-they are, in fact, dynamic and subject to evolutionary change. As sequences accumulate subtle…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Priyam Das, Sarah Robinson, Christine B Peterson, Luisa Cutillo
Model selection is a classic and well-studied statistical problem. It is a non-trivial challenge to select a model that balances the trade-off of model parsimony, fit, and generalizability. To paint a picture of this challenge in the high-dimensional setting, we begin by considering the lasso (), as a classic penalized…
Yusi Fan, Tian Wang, Zhiying Yan, Chang Liu + 7 more
- 1 College of Computer Science and Technology, Jilin University, Changchun, China, 130012. - 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China, 130012. - 3 Department of Chemical and Biological Engineering, The Hong Kong University of…
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We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
Raelynn Chen, Attri Ghosh, Jie Hu, Yong Chen + 2 more
High-dimensional biomedical datasets routinely contain sparse signals embedded among vast, correlated features, making variable selection central to building models that generalize. Although significance-based selection is widely used across modalities (e.g., imaging, EHR, multi-omics), statistical significance does…
Raelynn Chen, Attri Ghosh, Jie Hu, Yong Chen + 2 more
High-dimensional biomedical datasets routinely contain sparse signals embedded among vast, correlated features, making variable selection central to building models that generalize. Although significance-based selection is widely used across modalities (e.g., imaging, EHR, multi-omics), statistical significance does…
Andrea Polo-Rodríguez, David R. Penas, Julio R. Banga
Parameter estimation is a central challenge in systems biology, particularly for large dynamic models described by nonlinear ordinary differential equations (ODEs). These global optimization problems exhibit landscapes which are topologically heterogeneous, often exhibiting a pathological mixture of stiff, smooth…
Xuelei Wang, Jana Zweerings, Michael Lührs, Fengyu Cong + 5 more
Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be…
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Developing a transferable classical force field (FF) has historically been a lengthy, expert-informed process. In this work, we integrate optimization, machine learning, and data science techniques to accelerate the systematic design and parameterization of transferable FF models. As a demonstration, we create…
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We present an open source collection of scripts and programs for the setup, management and evaluation of calculations with the Vienna ab-initio simulation package (VASP), called utils4VASP. It contains 20 independent Python scripts and Fortran programs, all with a unified and intuitive handling concept based on command…
Mohamed Ghetas, Mohamed Abd Elaziz, Mohamed Issa
The presence of noisy, redundant, and irrelevant features in high-dimensional datasets significantly degrades the performance of classification models. Feature selection is a critical pre-processing step to mitigate this issue by identifying an optimal feature subset. While the Generalized Normal Distribution…
Alexander Goscinski, Christian A. Jorgensen, Victor Paul Principe, Guillaume Fraux + 6 more
Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their…
Álex Paz, Broderick Crawford, Eric Monfroy, Eduardo Rodriguez-Tello + 8 more
Bio-inspired metaheuristic optimization offers flexible search mechanisms for high-dimensional predictive problems under operational constraints. In administrative risk prediction settings, class imbalance and feature redundancy challenge conventional learning pipelines. This study evaluates a wrapper-based…
Mathieu Cherpitel, Thomas Bäck, Martijn R. Tannemaat, Anna V. Kononova
Unsupervised feature selection is commonly formulated as a multiobjective optimisation problem that jointly optimises subset quality and subset size. Yet the behaviour of this formulation depends critically on the choice of evaluation objective, the direction of subset-size regularisation, and the initialisation…
Fanwang Meng, Marco Martínez González, Valerii Chuiko, Alireza Tehrani + 7 more
Selector is a free, open-source Python library for selecting diverse subsets from any dataset, making it a versatile tool across a wide range of application domains. Selector implements different subset sampling algorithms based on sample distance, similarity, and spatial partitioning, along with metrics to quantify…
Swaroop, Maitreyi, Krishnamurti, Tamar + 2 more
We study the problem of selecting limited features to observe such that models trained on them can perform well simultaneously across multiple subpopulations. This problem has applications in settings where collecting each feature is costly, e.g. requiring adding survey questions or physical sensors, and we must be…
Abdelmonem M. Ibrahim, Doaa A. Fakhry, Fares Al-Shargie, Jan Cornelis
Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method…
Muhammad Rajabinasab, Arthur Zimek
Feature selection aims to identify the most informative and relevant features for a given dataset, either in terms of capturing the underlying data structure and distribution better, or with respect to the performance on a downstream task. Existing research in this area has largely focused on developing novel…
Muhammad Rajabinasab, Arthur Zimek
Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the curse of dimensionality by removing the redundant features, while unlike dimensionality reduction methods, preserving explainability. Feature…
Authors not listed
Machine learning potentials (MLPs) can help bridge the length- and time-scale gaps required to study diverse physicochemical phenomena in nanoporous materials with ab initio accuracy. These MLPs are typically trained on quantum chemical data obtained from traditional molecular dynamics (MD) simulations that…
Marc Sturrock, Anna Sturrock
Phenotypic selection can cause the transient, selective upregulation of fitness-conferring genes in isogenic cell populations under stress, producing selective enrichment of the fitness gene relative to a neutral reference gene. While computational models have shown that such enrichment requires noisy gene expression…
Malick Ebiele, Malika Bendechache, Rob Brennan
Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications. To validate the usefulness of a new method, it needs to be compared against at least one baseline method from the existing literature on a feature selection task using at least one dataset. Recent developments in…