24 papers · ranked by Valyu relevance
Palak Mahajan, Shahadat Uddin, Farshid Hajati, Mohammad Ali Moni + 1 more
'Joaquim Carreras'] Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have…
Hager Saleh, Eslam Amer, Tamer Abuhmed, Amjad Ali + 2 more
'Shaker El-Sappagh'] Alzheimer’s disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient’s multivariate time series data including…
Taichi Nukui, Akio Onogi
We developed an R package for stacking, which is an ensemble approach to supervised learning. Using this package, training and prediction of stacking can be conducted using one-row scripts. The R package stacking is available at the GitHub (https://github.com/Onogi/stacking). onogiakio@gmail.com This manuscript has no…
Angelos Chatzimparmpas, Rafael M. Martins, Kostiantyn Kucher, Andreas Kerren
Stacking methods (or stacked generalizations) refer to a group of ensemble learning methods where several base models are trained and combined into a metamodel with improved predictive power . obtaining reprints of this article, please send e-mail to: reprints@ieee.org. Digital Object Identifier…
Shijie Wang, Xueyong Zhang, Joanna Olbryś
With the development of financial technology, the traditional experience-based and single-network credit default prediction model can no longer meet the current needs. This manuscript proposes a credit default prediction model based on TabNeT-Stacking. First, use the PyTorch deep learning framework to construct an…
Taichi Nukui, Akio Onogi, Shanfeng Zhu
Ensemble learning is a promising approach to prediction tasks in biology (e.g. ). Stacking is an ensemble learning method that can be applied to various biological processes. One application is genomic prediction, a statistical technique for predicting phenotypes/genetic merits using genome-wide DNA markers. showed…
Harshika Sharma, Mansi Goel, Devkul Sahu, Mazhar Ayaz Sayed + 4 more
Accurate prediction of protein allergenicity is essential for ensuring food and drug safety. While machine learning and deep learning models have been explored for this task, limitations remain in dataset scale, feature representation, and model architecture. Here, we introduce AllerStack, a two-stage stacked ensemble…
Amjad Rehman, Teg Alam, Muhammad Mujahid, Faten S. Alamri + 3 more
'Bayan Al Ghofaily' 'Tanzila Saba' 'Natalia Kryvinska'] The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments…
Yuanyuan Wang, Zhuang Wu, Jing Gao, Chenjun Liu + 2 more
'Andrea Brunello'] With the growth of people’s demand for loans, banks and other financial institutions put forward higher requirements for customer credit risk level classification, the purpose is to make better loan decisions and loan amount allocation and reduce the pre-loan risk. This article proposes a Multi-Level…
Reza Bozorgpour
Accurate breast cancer risk prediction remains a central challenge in precision oncology due to the complexity and heterogeneity of underlying biological processes. While single-modality models based on clinical, gene expression, or copy number variation (CNV) data provide valuable prognostic insights, they often fail…
Yonghui Xu, Ruotong Meng, Xi Zhao, Santiago Marco + 1 more
Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction…
Xuan-Truc Dinh Tran, Tieu-Long Phan, Van-Thinh To, Ngoc-Vi Nguyen Tran + 4 more
3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in Drug Design. Our research summarized the key studies for applying 3D pharmacophore…
Achim Ahrens, Christian Hansen, Mark E. Schaffer
pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python's scikit-learn. Stacking combines multiple supervised machine learners—the "base" or "level-0" learners—into a single learner. The currently supported base learners include regularized regression, random…
Polina Proscura, Alexey Zaytsev
—Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance the quality and trustworthiness of ML solutions, and allow uncertainty estimation. However, they come at a price: training ensembles of…
Wei Wu, Liang Tang, Zhongjie Zhao, Chung‐Piaw Teo
computational geometry Authors: ['Wei Wu' 'Liang Tang' 'Zhongjie Zhao' 'Chung‐Piaw Teo'] Stacking, a potent ensemble learning method, leverages a meta-model to harness the strengths of multiple base models, thereby enhancing prediction accuracy. Traditional stacking techniques typically utilize established learning…
Roberto Aldave, Jean‐Pierre Dussault
The motivation of this work is to improve the performance of standard stacking approaches or ensembles, which are composed of simple, heterogeneous base models, through the integration of the generation and selection stages for regression problems. We propose two extensions to the standard stacking approach. In the…
Andrei V. Konstantinov, Lev V. Utkin
The gradient boosting machine is one of the powerful tools for solving regression problems. In order to cope with its shortcomings, an approach for constructing ensembles of gradient boosting models is proposed. The main idea behind the approach is to use the stacking algorithm in order to learn a second-level…
Yuxin Shen, Grzegorz Kudla, Diego A. Oyarzún
The growing demand for biological products drives many efforts to maximize expression of heterologous proteins. Advances in high-throughput sequencing can produce data suitable for building sequence-to-expression models with machine learning. The most accurate models have been trained on one-hot encodings, a…
Sean Whalen, Gaurav Pandey
—The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein…
Maya Ramchandran, Prasad Patil, Giovanni Parmigiani
Multi-study learning uses multiple training studies, separately trains classifiers on individual studies, and then forms ensembles with weights rewarding members with better cross-study prediction ability. This article considers novel weighting approaches for constructing tree-based ensemble learners in this setting.…
Authors not listed
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource consumption and enhancing the likelihood of clinical success via…
Esther Heid, Charles J. McGill, Florence H. Vermeire, William H. Green
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further…
Andrew McNutt, Yanjing Li, Paul Francoeur, David Koes
Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring…
Stephanie Wankowicz, James Fraser
In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting…