Advances in neural information processing systems 7

Weakly-supervised learning for lung carcinoma classification using deep learning

Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O., & Tsuneki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-66333-x · Cited by 250

Machine Learning Methods in Weather and Climate Applications: A Survey

Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., & Yang, Z. (2023) · doi:10.20944/preprints202309.1764.v2 · Cited by 246

A comprehensive survey of deep learning-based lightweight object detection models for edge devices

Mittal, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10877-1 · Cited by 244

DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

(2024) · doi:10.1038/s41467-024-45461-2 · Cited by 243

The role of population structure in computations through neural dynamics

Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F., & Ostojic, S. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01088-4 · Cited by 237

Towards reconstructing intelligible speech from the human auditory cortex

Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-37359-z · Cited by 221

Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities

Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I. W. M., Sanchez, C. I., Litjens, G., de Leeuw, F.-E., van Ginneken, B., Marchiori, E., & Platel, B. (2017) · Scientific Reports · doi:10.1038/s41598-017-05300-5 · Cited by 236

Biological underpinnings for lifelong learning machines

Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A. P., Chakravarthi Raja, S., Cheney, N., Clune, J., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00452-0 · Cited by 236

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36994-z · Cited by 231

Prostate cancer detection using deep convolutional neural networks

Yoo, S., Gujrathi, I., Haider, M. A., & Khalvati, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-55972-4 · Cited by 230

Hypernetwork Knowledge Graph Embeddings

Balažević, I., Allen, C., & Hospedales, T. M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30493-5_52 · Cited by 229

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives

Cascella, M., Semeraro, F., Montomoli, J., Bellini, V., Piazza, O., & Bignami, E. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02045-3 · Cited by 219

Studying user income through language, behaviour and affect in social media

Preoţiuc-Pietro, D., Volkova, S., Lampos, V., Bachrach, Y., & Aletras, N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138717 · Cited by 219

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02155-w · Cited by 217

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Nie, D., Lu, J., Zhang, H., Adeli, E., Wang, J., Yu, Z., Liu, L., Wang, Q., Wu, J., & Shen, D. (2019) · Scientific Reports · doi:10.1038/s41598-018-37387-9 · Cited by 213

A feature agnostic approach for glaucoma detection in OCT volumes

Maetschke, S., Antony, B., Ishikawa, H., Wollstein, G., Schuman, J., & Garnavi, R. (2019) · PLOS ONE · doi:10.1371/journal.pone.0219126 · Cited by 213

SecureBERT: A Domain-Specific Language Model for Cybersecurity

Aghaei, E., Niu, X., Shadid, W., & Al-Shaer, E. (2023) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-25538-0_3 · Cited by 210

A general prediction model for the detection of ADHD and Autism using structural and functional MRI

Sen, B., Borle, N. C., Greiner, R., & Brown, M. R. G. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194856 · Cited by 198

Learning from facial aging patterns for automatic age estimation

Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., & Dai, H. (2006) · Proceedings of the 14th ACM international conference on Multimedia · doi:10.1145/1180639.1180711 · Cited by 207

Convergence rate for consensus with delays

Nedić, A., & Ozdaglar, A. (2010) · Journal of Global Optimization · doi:10.1007/s10898-008-9370-2 · Cited by 198

Research screener: A machine learning tool to semi-automate abstract screening for systematic reviews

Chai, K. E. K., Lines, R. L. J., Gucciardi, D. F., & Ng, L. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01635-3 · Cited by 190

Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance

Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., & Herrera, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105590 · Cited by 196

A review of some techniques for inclusion of domain-knowledge into deep neural networks

Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022) · Scientific Reports · doi:10.1038/s41598-021-04590-0 · Cited by 196

Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review

Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195

DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

Réau, M., Renaud, N., Xue, L. C., & Bonvin, A. M. J. J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac759 · Cited by 190

Deep learning for small and big data in psychiatry

Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189

A computational reward learning account of social media engagement

Lindström, B., Bellander, M., Schultner, D. T., Chang, A., Tobler, P. N., & Amodio, D. M. (2021) · Nature Communications · doi:10.1038/s41467-020-19607-x · Cited by 185

Protein sequence design with a learned potential

Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171

Social influence and unfollowing accelerate the emergence of echo chambers

Sasahara, K., Chen, W., Peng, H., Ciampaglia, G. L., Flammini, A., & Menczer, F. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-020-00084-7 · Cited by 211

Three Epochs of Artificial Intelligence in Health Care

Howell, M. D., Corrado, G. S., & DeSalvo, K. B. (2024) · JAMA · doi:10.1001/jama.2023.25057 · Cited by 185

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Rasch, M. J., Mackin, C., Le Gallo, M., Chen, A., Fasoli, A., Odermatt, F., Li, N., Nandakumar, S. R., Narayanan, P., Tsai, H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40770-4 · Cited by 213

Deepfake generation and detection, a survey

Zhang, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11733-y · Cited by 176

Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD

Min, X., Yu, B., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-39071-y · Cited by 170

Position Information in Transformers: An Overview

Dufter, P., Schmitt, M., & Schütze, H. (2022) · Computational Linguistics · doi:10.1162/coli_a_00445 · Cited by 175

Medical imaging deep learning with differential privacy

Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., & Kaissis, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-93030-0 · Cited by 172

Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Costa-Neto, G., Fritsche-Neto, R., & Crossa, J. (2021) · Heredity · doi:10.1038/s41437-020-00353-1 · Cited by 153

Probabilistic movement modeling for intention inference in human–robot interaction

Wang, Z., Mülling, K., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913478447 · Cited by 171

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Spanier, K. I., Bravo González-Blas, C., Quan, X. J., Papasokrati, D., Hulselmans, G., Makhzami, S., et al. (2021) · doi:10.1101/2021.08.11.454937 · Cited by 170

Decoding gene regulation in the fly brain

Janssens, J., Aibar, S., Taskiran, I. I., Ismail, J. N., Gomez, A. E., Aughey, G., Spanier, K. I., De Rop, F. V., González-Blas, C. B., Dionne, M., et al. (2022) · Nature · doi:10.1038/s41586-021-04262-z · Cited by 170

SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Shen, X., Yuan, G., Ren, B., Tang, H., et al. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20083-0_37 · Cited by 169

Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances

Fiete, I. R., Fee, M. S., & Seung, H. S. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01311.2006 · Cited by 167

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14649 · Cited by 166

Cobolt: integrative analysis of multimodal single-cell sequencing data

Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168

Considerations for Evaluation and Generalization in Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2018) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-319-98131-4_1 · Cited by 176

Loss of plasticity in deep continual learning

Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature · doi:10.1038/s41586-024-07711-7 · Cited by 172

A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Peikari, M., Salama, S., Nofech-Mozes, S., & Martel, A. L. (2018) · Scientific Reports · doi:10.1038/s41598-018-24876-0 · Cited by 161

Exploring a latent cause theory of classical conditioning

Gershman, S. J., & Niv, Y. (2012) · Learning & Behavior · doi:10.3758/s13420-012-0080-8 · Cited by 149

Moral foundations twitter corpus: a collection of 35k tweets annotated for moral sentiment

Hoover, J., Portillo-Wightman, G., Yeh, L., Havaldar, S., Davani, A. M., Lin, Y., Kennedy, B., Atari, M., Kamel, Z., Mendlen, M., et al. (2020) · Social Psychological and Personality Science · doi:10.1177/1948550619876629 · Cited by 177

What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines?

Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2022) · doi:10.1101/2022.03.28.485868 · Cited by 160

Dynamic stochastic synapses as computational units.

Maass, W., & Zador, A. M. (1999) · Neural Computation · doi:10.1162/089976699300016494 · Cited by 149

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26643-8 · Cited by 147

Latency-Aware Collaborative Perception

Lei, Z., Ren, S., Hu, Y., Zhang, W., & Chen, S. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19824-3_19 · Cited by 147

A Comprehensive Study of Activity Recognition Using Accelerometers

Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I. (2018) · Informatics · doi:10.3390/informatics5020027 · Cited by 143

Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Haarburger, C., Müller-Franzes, G., Weninger, L., Kuhl, C., Truhn, D., & Merhof, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-69534-6 · Cited by 139

Recommendations and future directions for supervised machine learning in psychiatry

Cearns, M., Hahn, T., & Baune, B. T. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0607-2 · Cited by 127

Individual differences among deep neural network models

Mehrer, J., Spoerer, C. J., Kriegeskorte, N., & Kietzmann, T. C. (2020) · Nature Communications · doi:10.1038/s41467-020-19632-w · Cited by 133

Mechanisms Underlying the Neural Computation of Head Direction

Hulse, B. K., & Jayaraman, V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031516 · Cited by 130

A novel vision transformer model for skin cancer classification

Yang, G., Luo, S., & Greer, P. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11204-5 · Cited by 129

Fairness in Machine Learning: A Survey

Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646

Multimodal Federated Learning: A Survey

Che, L., Wang, J., Zhou, Y., & Ma, F. (2023) · doi:10.20944/preprints202307.1420.v1 · Cited by 127

Modeling of deformable objects for robotic manipulation: A tutorial and review

Arriola-Rios, V. E., Guler, P., Ficuciello, F., Kragic, D., Siciliano, B., & Wyatt, J. L. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00082 · Cited by 126

Challenges and future directions of secure federated learning: a survey

Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127

Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity

Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., & Shen, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00919-9_7 · Cited by 123

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Duan, R., Gao, L., Gao, Y., Hu, Y., Xu, H., Huang, M., Song, K., Wang, H., Dong, Y., Jiang, C., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009224 · Cited by 122

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies

Cohen, R., Elhadad, M., & Elhadad, N. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-10 · Cited by 121

Empirical data drift detection experiments on real-world medical imaging data

Kore, A., Abbasi Bavil, E., Subasri, V., Abdalla, M., Fine, B., Dolatabadi, E., & Abdalla, M. (2024) · Nature Communications · doi:10.1038/s41467-024-46142-w · Cited by 124

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain–Machine Interfaces

Pandarinath, C., Ames, K. C., Russo, A. A., Farshchian, A., Miller, L. E., Dyer, E. L., & Kao, J. C. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1669-18.2018 · Cited by 103

Tackling prediction uncertainty in machine learning for healthcare

Chua, M., Kim, D., Choi, J., Lee, N. G., Deshpande, V., Schwab, J., Lev, M. H., Gonzalez, R. G., Gee, M. S., & Do, S. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00988-x · Cited by 114

Symptoms of persistent complex bereavement disorder, depression, and PTSD in a conjugally bereaved sample: a network analysis

Malgaroli, M., Maccallum, F., & Bonanno, G. A. (2018) · Psychological Medicine · doi:10.1017/s0033291718001769 · Cited by 116

Investigating Gender Bias in BERT

Bhardwaj, R., Majumder, N., & Poria, S. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09881-2 · Cited by 112

Evaluating the use of large language model in identifying top research questions in gastroenterology

Lahat, A., Shachar, E., Avidan, B., Shatz, Z., Glicksberg, B. S., & Klang, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-31412-2 · Cited by 111

30Hz Object Detection with DPM V5

Sadeghi, M. A., & Forsyth, D. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_5 · Cited by 107

Wavelet-Improved Score-Based Generative Model for Medical Imaging

Wu, W., Wang, Y., Liu, Q., Wang, G., & Zhang, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3325824 · Cited by 105

A machine learning model that outperforms conventional global subseasonal forecast models

Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50714-1 · Cited by 105

A qualitative research framework for the design of user-centered displays of explanations for machine learning model predictions in healthcare

Barda, A. J., Horvat, C. M., & Hochheiser, H. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01276-x · Cited by 105

Neural correlates of sparse coding and dimensionality reduction

Beyeler, M., Rounds, E. L., Carlson, K. D., Dutt, N., & Krichmar, J. L. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006908 · Cited by 104

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine

Guo, L. L., Pfohl, S. R., Fries, J., Johnson, A. E. W., Posada, J., Aftandilian, C., Shah, N., & Sung, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-06484-1 · Cited by 103

A Review on Estimation of Distribution Algorithms

Larrañaga, P. (2002) · Genetic Algorithms and Evolutionary Computation · doi:10.1007/978-1-4615-1539-5_3 · Cited by 100

Technical and clinical overview of deep learning in radiology

Ueda, D., Shimazaki, A., & Miki, Y. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0795-3 · Cited by 100

Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images

Rawat, R. R., Ortega, I., Roy, P., Sha, F., Shibata, D., Ruderman, D., & Agus, D. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-64156-4 · Cited by 100

Explainable Artificial Intelligence: Evaluating the Objective and Subjective Impacts of xAI on Human-Agent Interaction

Silva, A., Schrum, M., Hedlund-Botti, E., Gopalan, N., & Gombolay, M. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2101698 · Cited by 104

A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2024) · Nature Communications · doi:10.1038/s41467-024-53147-y · Cited by 100

Toward an artificial intelligence physicist for unsupervised learning

Wu, T., & Tegmark, M. (2019) · Physical Review E · doi:10.1103/physreve.100.033311 · Cited by 99

Exploring the potential of using ChatGPT in physics education

Liang, Y., Zou, D., Xie, H., & Wang, F. L. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00273-7 · Cited by 98

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96

Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review

Lim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W. (2020) · Eye and Vision · doi:10.1186/s40662-020-00182-7 · Cited by 96

A meta-learning approach for genomic survival analysis

Qiu, Y. L., Zheng, H., Devos, A., Selby, H., & Gevaert, O. (2020) · Nature Communications · doi:10.1038/s41467-020-20167-3 · Cited by 96

ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills

Pardos, Z. A., & Bhandari, S. (2024) · PLOS ONE · doi:10.1371/journal.pone.0304013 · Cited by 97

APRIL: Active Preference Learning-Based Reinforcement Learning

Akrour, R., Schoenauer, M., & Sebag, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_8 · Cited by 95

Cracking the black box of deep sequence-based protein–protein interaction prediction

Bernett, J., Blumenthal, D. B., & List, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae076 · Cited by 95

Shortcut Learning of Large Language Models in Natural Language Understanding

Du, M., He, F., Zou, N., Tao, D., & Hu, X. (2024) · Communications of the ACM · doi:10.1145/3596490 · Cited by 94

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

Muñoz-Ortiz, A., Gómez-Rodríguez, C., & Vilares, D. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10903-2 · Cited by 94

Generative AI in Medical Imaging: Applications, Challenges, and Ethics

Koohi-Moghadam, M., & Bae, K. T. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01987-4 · Cited by 91

Applications of LLMs for Generating Cyber Security Exercise Scenarios

Mudassar Yamin, M., Hashmi, E., Ullah, M., & Katt, B. (2024) · IEEE Access · doi:10.1109/access.2024.3468914 · Cited by 91

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100628 · Cited by 90

A survey on online feature selection with streaming features

Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R. J., & Coley, C. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00708-3 · Cited by 87

3D molecular generative framework for interaction-guided drug design

(2024) · doi:10.1038/s41467-024-47011-2 · Cited by 88

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

Barbieri, S., Kemp, J., Perez-Concha, O., Kotwal, S., Gallagher, M., Ritchie, A., & Jorm, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-58053-z · Cited by 86