scikit-optimize/scikit-optimize

Domain Knowledge Injection in Bayesian Search for New Materials

Xie, Z., Evangelopoulos, X., Thacker, J. C. R., & Cooper, A. I. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230587 · Cited by 3

Atomistic descriptor optimization using complementary Euclidean and geodesic distance information

Iyer, G. R., & Rubenstein, B. M. (2024) · Molecular Physics · doi:10.1080/00268976.2024.2381617 · Cited by 3

A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG

Donos, C., Blidarescu, B., Pistol, C., Oane, I., Mı̂ndruță, I., & Barborică, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.946240 · Cited by 3

Phase ambiguity resolution and mixed pixel detection in EDM with multiple modulation wavelengths

Butt, J. A., Salido-Monzú, D., & Wieser, A. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.03.014 · Cited by 3

Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Martineau, T., He, S., Vaidyanathan, R., & Tan, H. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1111590 · Cited by 3

Physics-based crack formation model for René 80 in laser blown directed energy deposition: Theory and experiment

Kitt, A. L., Mohr, L., Kerwin, L., Bhaduri, A., Hosseinzadeh, H., Dhakad, A., Kmiotek, D., Shen, C. H., Huang, S., Kiedrowski, A., et al. (2023) · Additive manufacturing · doi:10.1016/j.addma.2023.103675 · Cited by 3

Development of an algorithm for identification of sown biodiverse pastures in Portugal

Morais, T. G., Rodrigues, N. R., Gama, I., Domingos, T., & Teixeira, R. F. M. (2023) · European Journal of Remote Sensing · doi:10.1080/22797254.2023.2238878 · Cited by 3

Two-stage feature selection for machine learning-aided DFT-based surface reactivity study on single-atom alloys

Ordillo, V. Z., Shimizu, K., Putungan, D. B., Santos-Putungan, A. B., Watanabe, S., de Leon, R. L., Ocon, J. D., Pilario, K. E., & Padama, A. A. B. (2024) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/1361-651x/ad53ee · Cited by 3

Learning to Optimize Black-Box Functions with Extreme Limits on the Number of Function Evaluations

Ansótegui, C., Sellmann, M., Shah, T., & Tierney, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92121-7_2 · Cited by 3

BOL e R o : Behavior optimization and learning for robots

Fabisch, A., Langosz, M., & Kirchner, F. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420913741 · Cited by 3

Toward stable, general machine-learned models of the atmospheric chemical systemPreprint

Kelp, M., Jacob, D. J., Kutz, J. N., Marshall, J. D., & Tessum, C. W. (2020) · doi:10.31223/osf.io/8vy6j · Cited by 3

First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method

Kreitmeir, M., Lago, B. V. P., Schoenfeld, L., Rehfeldt, S., & Klein, H. (2024) · Digital Chemical Engineering · doi:10.1016/j.dche.2024.100190 · Cited by 2

CheckMate: LLM-Powered Approximate Intermittent Computing

Sayyid-Ali, A.-R. I., Rafay, A., Soomro, M., Alizai, M. H., & Bhatti, N. A. (2025) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3715014.3722056 · Cited by 2

Service Life Prediction in Pipelines Using Machine Learning Techniques

Tawk, M., Mustapha, S., Trad, F. S., & Saad, G. A. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-01058-1 · Cited by 4

In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomes

Luo, L., Możejko, M., Markov, N. S., Peltekian, A., Mohsin, S., Carn, M., Cooper, P. R., Lysne, J., Joudi, A., Betensley, A. D., et al. (2026) · Scientific Reports · doi:10.1038/s41598-025-33809-7 · Cited by 4

Setup of a New Adaptive Fuzzy Particle Swarm Optimization Algorithm

Roy, N. A., Beauthier, C., & Mayer, A. (2022) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec55065.2022.9870387 · Cited by 2

A comparative analysis of Drug-Target Interaction detection approaches using Ensemble of Machine learning models

Baruah, S., & Devi, D. (2024) · International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB) · doi:10.1145/3640900.3640907 · Cited by 2

An automated OpenMP mutation testing framework for performance optimization

Miao, D., Laguna, I., Georgakoudis, G., Parasyris, K., & Rubio-González, C. (2024) · Parallel Computing · doi:10.1016/j.parco.2024.103097 · Cited by 2

Simultaneous Optimization of Crystal Plasticity Hardening Parameters

Shimanek, J. D., Liu, Z., & Beese, A. M. (2024) · JOM · doi:10.1007/s11837-024-06935-2 · Cited by 2

An iterative surrogate-based optimization approach for multi-server queuing system design

Pineda, C., Santana-Díaz, A., & Batres, R. (2025) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2025.103119 · Cited by 2

Full phase-velocity spectrum inversion of multimode Rayleigh waves

Niu, Y., Li, Y. E., & Chian, S. C. (2022) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/image2022-3746588.1 · Cited by 2

Interpretação de gestos de Libras usando k-means e Random Forest

Caiafa, E., Basilio, A., de Lima, A., & Araujo, G. (2023) · Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · doi:10.14209/sbrt.2023.1570923868 · Cited by 2

Understanding conformational dynamics from macromolecular crystal diffuse scatteringPreprint

Mazumder, P., & Ayyer, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.11.429988 · Cited by 2

The temporal limits of predicting fault failurePreprint

Wang, K., Johnson, C. W., Bennett, K. C., & Johnson, P. A. (2022) · doi:10.1002/essoar.10510792.1 · Cited by 2

Estimation of the Time for Steam Generator Trip Due to Cyber IntrusionsPreprint

Patel, J. K., Varuttamaseni, A., Youngblood, R., Lee, J. C., Wacker, S., Barbosa, R. P., & Guo, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4022939 · Cited by 2

Non-linear genetic regulation of the blood plasma proteomePreprint

Sigurdsson, A. I., Gräf, J. F., Yang, Z., Ravn, K., Meisner, J., Thielemann, R., Webel, H. E., Smit, R. A. J., Niu, L., Mann, M., et al. (2024) · medRxiv · doi:10.1101/2024.07.04.24309942 · Cited by 2

The potential of AI models to identify regulatory variants underlying cattle traitsPreprint

Zhao, R., Owen, R., Marr, M. M., Jayaraman, S., Hong, N. C., Talenti, A., Hassan, M., & Prendergast, J. G. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.01.606140 · Cited by 3

sv-channels: filtering genomic deletions using one-dimensional convolutional neural networksPreprint

Santuari, L., Georgievska, S., Kuzniar, A., Pedersen, B. S., Shneider, C., Mehrem, S. L., Ridder, L., Kloosterman, W. P., & de Ridder, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.17.618894 · Cited by 2

Assessment of the Choked Flow Model of RELAP5 for Application of Inverse Quantification Methods

Osés-Ezquerra, P., Freixa, J., Mendizábal, R., & Sánchez-Perea, M. (2024) · Nuclear Technology · doi:10.1080/00295450.2024.2411805 · Cited by 1

Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations

Maeng, J., Sung, J.-B., Kim, G.-H., Kim, J. W., Yum, K. S., & Park, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-80792-6 · Cited by 1

Enhancing federated averaging of self-supervised monocular depth estimators for autonomous vehicles with Bayesian optimization

Soares, E. F. D. S., Brazil, E. V., & Campos, C. A. V. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107752 · Cited by 1

Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions

Lai, Y., Zheng, P.-F., Ji, C., Li, Y., Zhang, S., Zhang, R.-T., Wang, Z., & Du, Y. (2025) · Proc. ACM Manag. Data · doi:10.1145/3709671 · Cited by 1

Stance classification: a comparative study and use case on Australian parliamentary debates

Ng, S., Zhang, J., Yu, S. S., Bhatti, A., Backholer, K., & Lim, C. P. (2025) · Journal of Computational Social Science · doi:10.1007/s42001-025-00366-y · Cited by 3

Detection of Diversion in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Williams, Q. J., Stewart, R. H., Ryan, E. D., Palmer, C., & Shields, A. (2025) · Nuclear Science and Engineering · doi:10.1080/00295639.2025.2560205 · Cited by 1

An Automated Data Efficient Morphometric Approach to Define Global Lentic and Lotic Inland Waters

Sharma, A., Narayanan, M., & Ilampooranan, I. (2026) · Water Resources Research · doi:10.1029/2025wr040137 · Cited by 1

Automated Detection of Side Channels in Cryptographic Protocols

Drees, J. P., Gupta, P., Hüllermeier, E., Jager, T., Konze, A., Priesterjahn, C., Ramaswamy, A., & Somorovsky, J. (2021) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3474369.3486868 · Cited by 1

Manipulating Causal Uncertainty in Sound Objects

Boger, T., Ananthabhotla, I., & Paradiso, J. A. (2021) · Audio Mostly Conference (AM) · doi:10.1145/3478384.3478405 · Cited by 1

Convolutional Neural Network for Binary Classification of Chromophobe Renal Cell Carcinoma and Oncocytoma

Cheng, A., Icaza, M., Judd, N., Smith, J. T., Mukherjee, S., Jain, M., & Wu, B. (2021) · Frontiers in Optics · doi:10.1364/fio.2021.jth5a.80 · Cited by 1

Optimal Experimental Design-based Reduced Order Modeling for Learning Optimal Aerodynamic Designs

Wang, L., Martins, J. R. R. A., & Du, X. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3716 · Cited by 1

Lateral optical force optimisation based on periodic arrays of a single motif

Gao, B., Gersen, H., & Hanna, S. (2023) · Optical Trapping and Optical Micromanipulation · doi:10.1117/12.2676718 · Cited by 1

Automated, rapid, and convergent hyperparameter optimizer (ARCH) for neural network classifiers

Mashayekhi, M., & Allen, T. T. (2024) · Quality Engineering · doi:10.1080/08982112.2024.2374888 · Cited by 1

On Sample Selection for Continual Learning: A Video Streaming Case Study

Dietmüller, A., Jacob, R., & Vanbever, L. (2024) · ACM SIGCOMM Computer Communication Review · doi:10.1145/3687234.3687237 · Cited by 3

Iterative machine learning control of the hollow cathode plume mode using Gaussian Process Regression

Brooks, J. W., Greve, C. M., & Georgin, M. P. (2025) · Journal of Electric Propulsion · doi:10.1007/s44205-025-00137-x · Cited by 1

Care coordination and patient safety outcome: a graph-based approach

Chen, H., Huang, Y., Yin, C., He, X., Ison, R. L., Bell, L., Opoku, R. A., Jiang, Z., Guo, S. J., Liu, M., et al. (2025) · npj Health Systems · doi:10.1038/s44401-025-00020-9 · Cited by 1

Robotic Interactive Physics Parameters Estimator (RIPPE)

Dehban, A., Cardoso, C., Vicente, P., Bernardino, A. J. M., & Santos-Victor, J. (2019) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2019.8850710 · Cited by 1

Using machine learning to speed up new and upgrade detector studies: a calorimeter case

Ratnikov, F., Деркач, Д., Boldyrev, A. S., Shevelev, A., Fakanov, P., & Matyushin, L. (2020) · EPJ Web of Conferences · doi:10.1051/epjconf/202024502019 · Cited by 1

Machine Learning Techniques

Dickinson, J. E. (2021) · Springer theses · doi:10.1007/978-3-030-86368-5_7 · Cited by 1

Listening tests for sound design: Faster optimization through lower-dimensional parameter spaces

Barker, T., Vieira, J., Pereira, F., Marques, R., & Campos, G. (2022) · Proceedings of DRS · doi:10.21606/drs.2022.729 · Cited by 1

A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small MoleculesPreprint

Scheen, J., Wu, W., Mey, A. S. J. S., Tosco, P., Mackey, M., & Michel, J. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12380612 · Cited by 1

Seismic fingerprint predicts ground motions during the 2018 Kilauea collapse sequencePreprint

Johnson, C. W., & Johnson, P. A. (2023) · Research Square · doi:10.21203/rs.3.rs-2500877/v1 · Cited by 1

Proceedings of the 15th International Workshop on Programming Models and Applications for Multicores and Manycores

(2024) · doi:10.1145/3649169 · Cited by 1

In silico perturbations provide multivariate interpretability in predicting post-lung transplant outcomesPreprint

Luo, L., Możejko, M., Markov, N. S., Peltekian, A., Mohsin, S., Carns, M. A., Cooper, P. R., Lysne, J., Joudi, A., Betensley, A. D., et al. (2024) · medRxiv · doi:10.1101/2024.10.19.24315817 · Cited by 1

Developmental and genetic modulation of evidence integration dynamics in zebrafish sensorimotor decision-making

Garza, R., Hady, A. E., & Bahl, A. (2026) · Communications Biology · doi:10.64898/2026.03.01.708829 · Cited by 1

Prediction of reaction conditions by deep learning techniques

Москалев, В. Б., & Putin, E. (2020) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2020-20-6-863-870

Active site sequence representation of human kinases outperforms full sequence for affinity prediction and inhibitor generation: 3D effects in a 1D modelPreprint

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · doi:10.33774/chemrxiv-2021-np7xj-v2

Bayesian Optimization of Hyperparameters in Kernel-Based Delay Rational Models

Treviso, F., Trinchero, R., & Canavero, F. (2021) · IEEE Electromagnetic Compatibility Magazine · doi:10.1109/memc.2021.9477255

Bayesian Optimization of Hyperparameters in Kernel-Based Delay Rational Models

Treviso, F., Trinchero, R., & Canavero, F. (2021) · IEEE Workshop on Signal and Power Integrity · doi:10.1109/spi52361.2021.9505195

Comment on gmd-2021-192

(2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-cec1

Comment on gmd-2021-192

(2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-rc1

Reply on CEC1

Lüthi, S. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-ac1

Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural RecordingsPreprint

Azeglio, S., Bernardo, A. D., Penna, G., Pittatore, F., Poetto, S., Gruenwald, J., Kapeller, C., Kamada, K., & Guger, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.04653 · Cited by 1

Comment on gmd-2021-192

(2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-rc2

Reply on RC2

Lüthi, S. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-ac3

Reply on RC1

Lüthi, S. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-192-ac2

Comment on gmd-2021-353

Barth, A., Azcarate, A. A., Troupin, C., et al. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-353-rc1

Comment on gmd-2021-353

Barth, A., Alvera-Azcárate, A., Troupin, C., et al. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-353-rc2

Reply on RC2

Cho, K. H. (2021) · Geoscientific model development · doi:10.5194/gmd-2021-139-ac2

The temporal limits of predicting fault failurePreprint

Wang, K., Johnson, C. W., Bennett, K. C., & Johnson, P. A. (2022) · doi:10.31223/x5w629

Reply on RC2

Barth, A. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-353-ac2

Reply on RC1

Barth, A. (2022) · Geoscientific model development · doi:10.5194/gmd-2021-353-ac1

Model for Moisture Infiltration in Vegetated Soil with Preferential Flow Oriented by Plant RootsPreprint

Mair, A., Dupuy, L. X., & Mariya Ptashnyk, B. (2022) · Research Square · doi:10.21203/rs.3.rs-1385615/v1

Active site sequence representation of human kinases outperforms full sequence for affinity prediction and inhibitor generation: 3D effects in a 1D modelPreprint

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · doi:10.33774/chemrxiv-2021-np7xj

Active site sequence representation of human kinases outperforms full sequence for affinity prediction and inhibitor generation: 3D effects in a 1D modelPreprint

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · doi:10.33774/chemrxiv-2021-np7xj-v3

Underestimation in temporal numerosity judgments computationally explained by population coding model

Kawabe, T., Ujitoko, Y., Yokosaka, T., & Kuroki, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-19941-8

Estimation of Fluid Saturation Using Machine Learning Algorithms: A Case Study of Niger Delta Sandstone Reservoirs

Adeeyo, Y. A., Osinaike, A. A., & Adun, G. O. (2023) · doi:10.2118/212696-ms

DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

Zhang, R., Wang, Y., Zhou, H., Li, B., & Xu, H. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads56603.2022.00122

Análise de fatores para redução de dimensionalidade em estudos de predição genômica

de Oliveira, C. F. (2022) · doi:10.47328/ufvbbt.2023.026

Estimating the received solar irradiances by traditional vaulted roofs using optimized neural networks and transfer learning

Ayoub, M. (2023) · International Journal of Architectural Computing · doi:10.1177/14780771231186259

Integrating Geometric Metamodel-Assisted Process Assurance into Topology Optimization of Low-Pressure Die Castings

Rosnitschek, T., Erber, M., Hartmann, C., Alber-Laukant, B., Volk, W., & Tremmel, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-33758-1_9

A Sustainable West? Analyzing Clusters of Public Opinion in Sustainability Western Discourses in a Collection of Multilingual Newspapers (1999-2018)

Fernández, E. F., & Savcisens, G. (2023) · Digital Humanities in the Nordic and Baltic Countries Publications · doi:10.5617/dhnbpub.10660

D4W: Dependable Data-Driven Dynamics for Wheeled Robots

Lin, Y., Liu, M., & Yu, Y. H. (2023) · International Conference on Distributed Artificial Intelligence (DAI) · doi:10.1145/3627676.3627684

Sistema antifraude para detecção de não conformidades em gastos corporativos

Ribeiro, L. G. (2024) · doi:10.11606/d.55.2024.tde-11072024-142612

Readmission Risk Prediction after Total Hip Arthroplasty Using Machine Learning and Hyperparameters Optimized with Bayesian OptimizationPreprint

Purbasari, I. Y., Bayuseno, A. P., Isnanto, R. R., & Winarni, T. I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5071220

Solving self-calibration of ALMA data with an optimization method

Ikeda, S., Nakazato, T., Tsukagoshi, T., Takeuchi, T. T., & Yamaguchi, M. (2024) · Publications of the Astronomical Society of Japan · doi:10.1093/pasj/psae114

Estimating Fluid Saturation Using Machine Learning: A Case Study of Niger Delta Sandstone Reservoirs

Adeeyo, Y. A., & Osinaike, A. A. (2025) · SPE Gulf Oil and Gas Technology Conference (GOTECH) · doi:10.2118/224648-ms

A New Hybrid Machine Learning Method for Stellar Parameter Inference

Shankar, S., Gully-Santiago, M., & Morley, C. V. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adcb47

Noah‐MP Parameter Optimization at Southern Great Plains Using Bayesian Optimization

Wang, Q., Crowell, S., & Klein, P. M. (2025) · Journal of Geophysical Research Atmospheres · doi:10.1029/2024jd041793

A Bayesian Framework for Physiologically-Based Modeling of Flutter-Induced Aneurysm ProgressionPreprint

Bhattacharyya, K. (2026) · medRxiv · doi:10.64898/2026.02.09.26345810

Sensor Reduction for Diversion Detection in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Williams, Q. J., Stewart, R. H., & Palmer, C. (2026) · Nuclear Science and Engineering · doi:10.1080/00295639.2026.2619313

Bayesian optimisation of a roof extension/spoiler on a simplified vehicle, employing wall-resolved large eddy simulation

Janczuk, K., Gaylard, A. P., & Morgans, A. S. (2026) · Journal of Fluid Mechanics · doi:10.1017/jfm.2026.11244

pyDielectriX: an open-source Python package for modeling dielectric response in materials

Rentería-Baltiérrez, F. Y., Puente-Córdova, J. G., & Miranda-Valdez, I. Y. (2026) · Engineering With Computers · doi:10.1007/s00366-026-02338-w

GraphSAGEnES: A Graph Neural Network Designed for Graphs with Low Feature Discriminability

Bernhardt, B. D., Marciano, C., & Guarracino, M. R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21480-5_19

Bayesian Optimization of Cold Sintering Process for High-Conductivity LATP Solid-State Electrolytes

Rajapriya, N., Shin, W., & Hamamoto, K. (2026) · ACS Applied Energy Materials · doi:10.1021/acsaem.6c01362

The shape of evolution: persistent homology of genetic-distance data as an observable of reticulate processes in pathogen and plant genomesPreprint

Feged-Rivadeneira, A., González-Casabianca, F., Cárdenas, P., Ángel, A., & Corredor, V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.18.733071

Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State DecodingPreprint

Wang, X., Zweerings, J., Lührs, M., Cong, F., Mathiak, K., Linden, D. E. J., Goebel, R., Ciarlo, A., & Mehler, D. M. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.729835

Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning–Based Predictive Model Development and Temporal Validation Study

Kravchenko, O., Halvorsen, M., Boberg, J., Kaldo, V., Crowley, J. J., Kuja‐Halkola, R., Rück, C., & Wallert, J. (2026) · Journal of Medical Internet Research · doi:10.2196/100162

Gut microbiome signatures associate with DNA methylation-based biological aging

Kunihiro, B. P., Yamamoto, B. Y., Juárez, R., & Maunakea, A. K. (2026) · Scientific Reports · doi:10.1038/s41598-026-52974-x · Cited by 1

Random Forest-Based Prediction of Geothermal Heat Flow in the Arabian-Nubian Shield: A Multi-Proxy Satellite Geophysical ApproachPreprint

Haggag, M., Sobh, M., & Ghazala, H. H. (2026) · Research Square · doi:10.21203/rs.3.rs-10336715/v1

Multi-output machine-learning benchmark and Pareto-based decision support for electron-beam welding of thin-walled titanium structures with comparative synthetic-data augmentation

Tynchenko, V. V., Kurashkin, S. O., Bukhtoyarov, V. V., Connie, T., Бородулин, А. С., Mikhailov, A. Y., & Тынченко, В. С. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18892-y

Screening of large covalent organic frameworks databases for urea removal from water using molecular simulations and machine learning

Dehaghani, M. Z., Fabiani, T., Tsaftaris, S., Dimartino, S., & Angelis, M. G. D. (2026) · Digital Discovery · doi:10.1039/d6dd00159a

holgern/scikit-optimize

Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10804382 · Cited by 2

scikit-package version 0.2.1

contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768434

scikit-package version 0.1.0

contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768404

C-OPT: composite optimization in Python

Pedregosa, F., & Seong, L. K. (2017) · Figshare · doi:10.5281/zenodo.597854 · Cited by 4

A Data-Centric Optimization Workflow for the Python Language

Ziogas, A. N. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000586638