Advances in neural information processing systems 7

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation

Bravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01316-4 · Cited by 85

Towards Scaling Fully Personalized PageRank

Fogaras, D., & Rácz, B. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30216-2_9 · Cited by 84

Overlapping cell nuclei segmentation in microscopic images using deep belief networks

Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., & Ahuja, C. (2016) · Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3009977.3010043 · Cited by 80

Prognostication and risk factors for cystic fibrosis via automated machine learning

Alaa, A. M., & van der Schaar, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-29523-2 · Cited by 80

Automated annotation of birdsong with a neural network that segments spectrograms

Cohen, Y., Nicholson, D. A., Sanchioni, A., Mallaber, E. K., Skidanova, V., & Gardner, T. J. (2022) · eLife · doi:10.7554/elife.63853 · Cited by 80

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias

Gallifant, J., Zhang, J., del Pilar Arias Lopez, M., Zhu, T., Camporota, L., Celi, L. A., & Formenti, F. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.09.025 · Cited by 72

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

Pokuri, B. S. S., Ghosal, S., Kokate, A., Sarkar, S., & Ganapathysubramanian, B. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0231-y · Cited by 73

Scaling laws in the mammalian neocortex: Does form provide clues to function?

Harrison, K. H., Hof, P. R., & Wang, S. S.-H. (2002) · Journal of Neurocytology · doi:10.1023/a:1024178127195 · Cited by 78

Spaced learning enhances episodic memory by increasing neural pattern similarity across repetitions

Feng, K., Zhao, X., Liu, J., Cai, Y., Ye, Z., Chen, C., & Xue, G. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2741-18.2019 · Cited by 78

Moral consideration for AI systems by 2030

Sebo, J., & Long, R. (2025) · AI and Ethics · doi:10.1007/s43681-023-00379-1 · Cited by 80

Cross and learn: Cross-modal self-supervision

Sayed, N., Brattoli, B., & Ommer, B. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-12939-2_17 · Cited by 76

Explainable machine learning for public policy: Use cases, gaps, and research directions

Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics

Aitchison, L., & Lengyel, M. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005186 · Cited by 67

Deep learning in radiology: ethics of data and on the value of algorithm transparency, interpretability and explainability

Fernandez-Quilez, A. (2023) · AI and Ethics · doi:10.1007/s43681-022-00161-9 · Cited by 66

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Seal, D. B., Das, V., Goswami, S., & De, R. K. (2020) · Genomics · doi:10.1016/j.ygeno.2020.03.021 · Cited by 69

A survey of model compression strategies for object detection

Lyu, Z., Yu, T., Pan, F., Zhang, Y., Luo, J., Zhang, D., Chen, Y., Zhang, B., & Li, G. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17192-x · Cited by 65

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

Rafferty, C., Johnson, K., O'Mahony, J., Burgoyne, B., Rea, R., & Balss, K. M. (2020) · Biotechnology Progress · doi:10.1002/btpr.2977 · Cited by 63

Human representation learning

Radulescu, A., Shin, Y. S., & Niv, Y. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092920-120559 · Cited by 63

Augmenting interpretable models with large language models during training

Singh, C., Askari, A., Caruana, R., & Gao, J. (2023) · Nature Communications · doi:10.1038/s41467-023-43713-1 · Cited by 63

A co-design framework of neural networks and quantum circuits towards quantum advantage

Jiang, W., Xiong, J., & Shi, Y. (2021) · Nature Communications · doi:10.1038/s41467-020-20729-5 · Cited by 63

Equivariant Graph Neural Networks for Toxicity Prediction

Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00032 · Cited by 62

Truth machines: synthesizing veracity in AI language models

Munn, L., Magee, L., & Arora, V. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01756-4 · Cited by 65

Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond

Xie, Q., Schenck, E. J., Yang, H. S., Chen, Y., Peng, Y., & Wang, F. (2023) · doi:10.21203/rs.3.rs-3661764/v1 · Cited by 57

Guided Random Forests for Identification of Key Fetal Anatomy and Image Categorization in Ultrasound Scans

Yaqub, M., Kelly, B., Papageorghiou, A. T., & Noble, J. A. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24574-4_82 · Cited by 56

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Jebali, F., Majumdar, A., Turck, C., Harabi, K.-E., Faye, M.-C., Muhr, E., Walder, J.-P., Bilousov, O., Michaud, A., Vianello, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-44766-6 · Cited by 58

Optomotor Swimming in Larval Zebrafish Is Driven by Global Whole-Field Visual Motion and Local Light-Dark Transitions

Kist, A. M., & Portugues, R. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.024 · Cited by 55

Intelligent Robotic Perception Systems

Premebida, C., Ambrus, R., & Marton, Z.-C. (2019) · Applications of Mobile Robots · doi:10.5772/intechopen.79742 · Cited by 52

Best practice for single-trial detection of event-related potentials: application to brain-computer interfaces

Cecotti, H., & Ries, A. J. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2016.07.500 · Cited by 50

Automatic Subtyping of Individuals with Primary Progressive Aphasia

Themistocleous, C., Ficek, B., Webster, K., den Ouden, D.-B., Hillis, A. E., & Tsapkini, K. (2021) · Journal of Alzheimer’s Disease · doi:10.3233/jad-201101 · Cited by 51

Deep Big Multilayer Perceptrons for Digit Recognition

Cireşan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_31 · Cited by 48

Large language models, social demography, and hegemony: comparing authorship in human and synthetic text

Alvero, A. J., Lee, J., Regla-Vargas, A., Kizilcec, R. F., Joachims, T., & Antonio, A. L. (2024) · Journal of Big Data · doi:10.1186/s40537-024-00986-7 · Cited by 48

CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging

Hamamci, I. E., Er, S., & Menze, B. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72390-2_45 · Cited by 47

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Smith, Z., Strobel, M., Vani, B. P., & Tiwary, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01698 · Cited by 47

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · doi:10.20944/preprints201803.0147.v1 · Cited by 143

Language models: past, present, and future

Li, H. (2022) · Communications of the ACM · doi:10.1145/3490443 · Cited by 45

Spectral Variability in the Aged Brain during Fine Motor Control

Quandt, F., Bönstrup, M., Schulz, R., Timmermann, J. E., Zimerman, M., Nolte, G., & Hummel, F. C. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00305 · Cited by 38

Modality-transfer generative adversarial network and dual-level unified latent representation for visible thermal Person re-identification

Fan, X., Jiang, W., Luo, H., & Mao, W. (2022) · The Visual Computer · doi:10.1007/s00371-020-02015-z · Cited by 45

Analysing the impact of ChatGPT in research

Picazo-Sanchez, P., & Ortiz-Martin, L. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05298-0 · Cited by 46

Learning probabilistic neural representations with randomly connected circuits

Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44

Applying machine learning to estimate the optical properties of black carbon fractal aggregates

Luo, J., Zhang, Y., Wang, F., Wang, J., & Zhang, Q. (2018) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2018.05.002 · Cited by 44

Predictive coding networks for temporal prediction

Millidge, B., Tang, M., Osanlouy, M., Harper, N. S., & Bogacz, R. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011183 · Cited by 44

Human Motion prediction based on attention mechanism

Sang, H.-F., Chen, Z.-Z., & He, D.-K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08269-7 · Cited by 42

Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications

Schoeller, F., Horowitz, A. H., Jain, A., Maes, P., Reggente, N., Christov-Moore, L., Pezzulo, G., Barca, L., Allen, M., Salomon, R., et al. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105478 · Cited by 42

Insider threat detection for specific threat scenarios

Tian, T., Zhang, C., Jiang, B., Feng, H., & Lu, Z. (2025) · Cybersecurity · doi:10.1186/s42400-024-00321-w · Cited by 42

Extending and Analyzing Self-supervised Learning Across Domains

Wallace, B., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_43 · Cited by 35

Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

Guo, L., Zhu, J., Wang, K., Cheng, K.-K., Xu, J., Dong, L., Xu, X., Chen, C., Shah, M., Peng, Z., et al. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02002 · Cited by 36

Applying large language models for automated essay scoring for non-native Japanese

Li, W., & Liu, H. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03209-9 · Cited by 36

Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering

Minot, M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2023.12.003 · Cited by 35

The exact equivalence of distance and kernel methods in hypothesis testing

Shen, C., & Vogelstein, J. T. (2021) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-020-00378-1 · Cited by 41

A retrieval-augmented chatbot based on GPT-4 provides appropriate differential diagnosis in gastrointestinal radiology: a proof of concept study

Rau, S., Rau, A., Nattenmüller, J., Fink, A., Bamberg, F., Reisert, M., & Russe, M. F. (2024) · European Radiology Experimental · doi:10.1186/s41747-024-00457-x · Cited by 33

Causal Structure Learning: A Combinatorial Perspective

Squires, C., & Uhler, C. (2023) · Foundations of Computational Mathematics · doi:10.1007/s10208-022-09581-9 · Cited by 31

DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

Xie, J., Chen, S., Lei, J., & Yang, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01466 · Cited by 31

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Tiulpin, A., Klein, S., Bierma-Zeinstra, S. M. A., Thevenot, J., Rahtu, E., Meurs, J. V., Oei, E. H. G., & Saarakkala, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-56527-3 · Cited by 31

Digital forgetting in large language models: a survey of unlearning methods

Blanco-Justicia, A., Jebreel, N., Manzanares-Salor, B., Sánchez, D., Domingo-Ferrer, J., Collell, G., & Eeik Tan, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11078-6 · Cited by 31

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments

Chmiel, F. P., Burns, D. K., Azor, M., Borca, F., Boniface, M. J., Zlatev, Z. D., White, N. M., Daniels, T. W. V., & Kiuber, M. (2020) · doi:10.1101/2020.12.02.20239194 · Cited by 30

Defending against Backdoors in Federated Learning with Robust Learning Rate

Ozdayi, M. S., Kantarcioglu, M., & Gel, Y. R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17118 · Cited by 30

Structure-informed protein language models are robust predictors for variant effects

(2024) · doi:10.1007/s00439-024-02695-w · Cited by 30

Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution

Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S.-Y., Gruntman, E., Macke, J. H., & Turaga, S. C. (2023) · doi:10.1101/2023.03.11.532232 · Cited by 31

A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Kim, Y.-G., Choi, G., Go, H., Cho, Y., Lee, H., Lee, A.-R., Park, B., & Kim, N. (2019) · Scientific Reports · doi:10.1038/s41598-019-41479-5 · Cited by 29

scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H. (2025) · Nature Communications · doi:10.1038/s41467-025-60333-z · Cited by 29

Baryon acoustic oscillations reconstruction using convolutional neural networks

Mao, T.-X., Wang, J., Li, B., Cai, Y.-C., Falck, B., Neyrinck, M., & Szalay, A. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3741 · Cited by 29

Explainable AI decision support improves accuracy during telehealth strep throat screening

Gomez, C., Smith, B.-L., Zayas, A., Unberath, M., & Canares, T. (2024) · Communications Medicine · doi:10.1038/s43856-024-00568-x · Cited by 30

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Jamal, S., Wimmer, H., & Sarker, I. (2023) · doi:10.21203/rs.3.rs-3608294/v1 · Cited by 27

Learning the language of protein-protein interactions

(2026) · doi:10.1038/s41467-025-67971-3 · Cited by 26

A framework for evaluating clinical artificial intelligence systems without ground-truth annotations

Kiyasseh, D., Cohen, A., Jiang, C., & Altieri, N. (2024) · Nature Communications · doi:10.1038/s41467-024-46000-9 · Cited by 26

TIS Transformer: remapping the human proteome using deep learning

Clauwaert, J., McVey, Z., Gupta, R., & Menschaert, G. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad021 · Cited by 26

Treatment effect prediction with adversarial deep learning using electronic health records

Chu, J., Dong, W., Wang, J., He, K., & Huang, Z. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01151-9 · Cited by 24

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30

Nonlocality and Nonlinearity Implies Universality in Operator Learning

Lanthaler, S., Li, Z., & Stuart, A. M. (2023) · arXiv · doi:10.48550/arxiv.2304.13221 · Cited by 25

PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Yu, H., & Welch, J. D. (2022) · doi:10.1101/2022.07.20.500854 · Cited by 57

An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients

Kwon, H., Clifford, G. D., Genias, I., Bernhard, D., Esper, C. D., Factor, S. A., & McKay, J. L. (2023) · Sensors · doi:10.3390/s23041766 · Cited by 24

Retro Drug Design: From Target Properties to Molecular Structures

Wang, Y., Michael, S., Yang, S.-M., Huang, R., Cruz-Gutierrez, K., Zhang, Y., Zhao, J., Xia, M., Shinn, P., & Sun, H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00123 · Cited by 24

Interpretable Deep-Learning Unveils Structure–Property Relationships in Polybenzenoid Hydrocarbons

Weiss, T., Wahab, A., Bronstein, A. M., & Gershoni-Poranne, R. (2023) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.2c02381 · Cited by 24

Hybrid protein–ligand binding residue prediction with protein language models: does the structure matter?

Gamouh, H., Novotný, M., & Hoksza, D. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf431 · Cited by 23

Hierarchical generative modelling for autonomous robots

Yuan, K., Sajid, N., Friston, K., & Li, Z. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00752-z · Cited by 23

Medical machine learning operations: A framework to facilitate clinical AI development and deployment in radiology

de Almeida, J. G., Messiou, C., Withey, S. J., Matos, C., Koh, D.-M., & Papanikolaou, N. (2025) · European Radiology · doi:10.1007/s00330-025-11654-6 · Cited by 23

LLM-KGMQA: large language model-augmented multi-hop question-answering system based on knowledge graph in medical field

Wang, F., Shi, D., Aguilar, J., Cui, X., Jiang, J., Shen, L., & Li, M. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02399-1 · Cited by 23

Quality indices for topic model selection and evaluation: a literature review and case study

Meaney, C., Stukel, T. A., Austin, P. C., Moineddin, R., Greiver, M., & Escobar, M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02216-1 · Cited by 23

This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks

Booker, W. W., Ray, D. D., & Schrider, D. R. (2023) · GENETICS · doi:10.1093/genetics/iyad063 · Cited by 22

MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

Meng, C., Trinh, L., Xu, N., & Liu, Y. (2021) · doi:10.21203/rs.3.rs-402058/v1 · Cited by 21

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21

ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems

Harrison, J., Garg, A., Ivanovic, B., Zhu, Y., Savarese, S., Fei-Fei, L., & Pavone, M. (2017) · arXiv · doi:10.48550/arxiv.1707.04674 · Cited by 21

Mobile-kube: mobility-aware and energy-efficient service orchestration on kubernetes edge servers

Ghafouri, S., Karami, A., Bakhtiarvan, D. B., Bigdeli, A. S., Gill, S. S., & Doyle, J. (2022) · 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00019 · Cited by 20

An exploration of automated narrative analysis via machine learning

Jones, S., Fox, C., Gillam, S., & Gillam, R. B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0224634 · Cited by 20

Hallucinating structure-conditioned antibody libraries for target-specific binders

Mahajan, S. P., Ruffolo, J. A., Frick, R., & Gray, J. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.999034 · Cited by 20

Neural Scaling of Deep Chemical Models

Frey, N., Soklaski, R., Axelrod, S., Samsi, S., Gomez-Bombarelli, R., Coley, C., & Gadepally, V. (2022) · doi:10.26434/chemrxiv-2022-3s512 · Cited by 20

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology

(2024) · doi:10.1093/bib/bbae004 · Cited by 20

Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability

Tan, Y., Zhou, B., Zheng, L., Fan, G., & Hong, L. (2025) · eLife · doi:10.7554/elife.98033.4 · Cited by 20

Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning

Venturini, L., Papageorghiou, A. T., Noble, J. A., & Namburete, A. I. L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59710-8_67 · Cited by 18

Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

Li, Y.-F., Pan, X., & Shen, H.-B. (2025) · Patterns · doi:10.1016/j.patter.2025.101262 · Cited by 18

MultiCPA: Multimodal Compositional Perturbation Autoencoder

Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16

Towards perturbation prediction of biological networks using deep learning

Li, D., & Gao, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-48391-y · Cited by 16

Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Matsumoto, T., Walston, S. L., Walston, M., Kabata, D., Miki, Y., Shiba, M., & Ueda, D. (2022) · Journal of Digital Imaging · doi:10.1007/s10278-022-00691-y · Cited by 15

Measuring and Mitigating Gender Bias in Legal Contextualized Language Models

Bozdag, M., Sevim, N., & Koç, A. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3628602 · Cited by 15

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

Platero-Rochart, D., Krivobokova, T., Gastegger, M., Reibnegger, G., & Sánchez-Murcia, P. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00772 · Cited by 15

Modular large language model agents for multi-task computational materials science

Chaudhari, A., Ock, J., & Barati Farimani, A. (2026) · Communications Materials · doi:10.1038/s43246-025-00994-x · Cited by 19

Knowledge acquisition with forgetting: an incremental and developmental setting

Martínez-Plumed, F., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2015) · Adaptive Behavior · doi:10.1177/1059712315608675 · Cited by 14

Knot or not? Identifying unknotted proteins in knotted families with sequence‐based Machine Learning model

Sikora, M., Klimentova, E., Uchal, D., Sramkova, D., Perlinska, A. P., Nguyen, M. L., Korpacz, M., Malinowska, R., Nowakowski, S., Rubach, P., et al. (2024) · Protein Science · doi:10.1002/pro.4998 · Cited by 13

A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips

(2023) · doi:10.3390/s23218924 · Cited by 13

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Haq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08167-y · Cited by 12