Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512

Untitled work

(2011) · Cited by 6,993

Prototypical Networks for Few-shot LearningPreprint

Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 6,245

The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370

Matching Networks for One Shot LearningPreprint

Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04080 · Cited by 4,975

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

Patient-derived xenograft models in cancer therapy: technologies and applications

Liu, Y., Wu, W., Cai, C., Zhang, H., Shen, H., & Han, Y. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01419-2 · Cited by 533

CORUM: the comprehensive resource of mammalian protein complexes–2022

Tsitsiridis, G., Steinkamp, R., Giurgiu, M., Brauner, B., Fobo, G., Frishman, G., Montrone, C., & Ruepp, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1015 · Cited by 278

Big data in basic and translational cancer research

Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Partin, A., Brettin, T. S., Zhu, Y., Narykov, O., Clyde, A., Overbeek, J., & Stevens, R. L. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1086097 · Cited by 155

Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas

Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139

Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review

Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03273-7 · Cited by 122

Image-based cell phenotyping with deep learning

Pratapa, A., Doron, M., & Caicedo, J. C. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.001 · Cited by 105

Few-shot learning for medical text: A review of advances, trends, and opportunities

Ge, Y., Guo, Y., Das, S., Al-Garadi, M. A., & Sarker, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104458 · Cited by 107

A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening

He, D., Liu, Q., Wu, Y., & Xie, L. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00541-0 · Cited by 100

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer

Sherman, M. A., Yaari, A. U., Priebe, O., Dietlein, F., Loh, P.-R., & Berger, B. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01353-8 · Cited by 80

Predicting and characterizing a cancer dependency map of tumors with deep learning

Chiu, Y.-C., Zheng, S., Wang, L.-J., Iskra, B. S., Rao, M. K., Houghton, P. J., Huang, Y., & Chen, Y. (2021) · Science Advances · doi:10.1126/sciadv.abh1275 · Cited by 84

Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

Xu, L., Zhang, S., Li, X., Tang, M., Xie, P., & Hong, X. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202106880 · Cited by 83

Few-shot classification of ultrasound breast cancer images using meta-learning algorithms

Işık, G., & Paçal, İ. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09767-y · Cited by 75

Hallmarks of artificial intelligence contributions to precision oncology

Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83

Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis

Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75

An overview of machine learning methods for monotherapy drug response prediction

Firoozbakht, F., Yousefi, B., & Schwikowski, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab408 · Cited by 70

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset

Pałczyński, K., Śmigiel, S., Ledziński, D., & Bujnowski, S. (2022) · Sensors · doi:10.3390/s22030904 · Cited by 76

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models

Sharifi-Noghabi, H., Jahangiri, S., Smirnov, P., Hon, C. S.-Y., Mammoliti, A., Nair, S. K., Mer, A. S., Ester, M., & Haibe‐Kains, B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab294 · Cited by 66

FSL-Kla: A few-shot learning-based multi-feature hybrid system for lactylation site prediction

Jiang, P., Ning, W., Shi, Y., Liu, C., Mo, S., Zhou, H., Liu, K., & Guo, Y. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.013 · Cited by 62

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Fernández-Torras, A., Duran-Frigola, M., Bertoni, M., Locatelli, M., & Aloy, P. (2022) · Nature Communications · doi:10.1038/s41467-022-33026-0 · Cited by 59

Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions

Peng, W., Liu, H., Dai, W., Ning, Y., & Wang, J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac574 · Cited by 62

Beyond metabolic waste: lysine lactylation and its potential roles in cancer progression and cell fate determination

Wang, J., Mao, L., Wang, J., Zhang, X., Wu, M. C., Wen, Q., & Yu, S. (2023) · Cellular Oncology · doi:10.1007/s13402-023-00775-z · Cited by 58

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50

Disturbed-entropy: A simple data quality assessment approach

Li, Y., Chao, X., & Erċışlı, S. (2022) · ICT Express · doi:10.1016/j.icte.2022.01.006 · Cited by 50

Machine learning applications for therapeutic tasks with genomics data

Huang, K., Xiao, C., Glass, L. M., Critchlow, C. W., Gibson, G. C., & Sun, J. (2021) · Patterns · doi:10.1016/j.patter.2021.100328 · Cited by 46

Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions

Cai, Y., Gong, D.-X., Tang, L.-Y., Cai, Y. H., Li, H., Jing, T.-C., Gong, M., Hu, W., Zhang, Z.-W., Zhang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/47645 · Cited by 54

PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid

Zhou, Y., Dai, Q., Xu, Y., Wu, S., Cheng, M., & Zhao, B. T. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01082-6 · Cited by 50

Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery

Pallikkavaliyaveetil, N., & Chandrasekaran, S. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115762 · Cited by 57

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Mourragui, S. M. C., Loog, M., Vis, D. J., Moore, K., Manjón, A. G., van de Wiel, M. A., Reinders, M. J. T., & Wessels, L. F. A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106682118 · Cited by 41

OncoLoop: A Network-Based Precision Cancer Medicine Framework

Vasciaveo, A., Arriaga, J. M., de Almeida, F. N., Zou, M., Douglass, E. F., Picech, F., Shibata, M., Rodríguez-Calero, A., de Brot, S., Mitrofanova, A., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0342 · Cited by 38

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction

Wang, H., Dai, C., Wen, Y., Wang, X., Liu, W., He, S., Bo, X., & Peng, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac501 · Cited by 40

How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

Chen, Y., & Zhang, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab378 · Cited by 40

Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction

Sharifi-Noghabi, H., Harjandi, P. A., Zolotareva, O. I., Collins, C. C., & Ester, M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00408-w · Cited by 39

Exploring Clean Label Backdoor Attacks and Defense in Language Models

Zhao, S., Tuan, L. A., Fu, J., Wen, J., & Luo, W. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3407571 · Cited by 46

Learning and actioning general principles of cancer cell drug sensitivity

Carli, F., Chiaro, P. D., Morelli, M., Arora, C., Bisceglia, L., Rosa, N. D. O., Cortesi, A., Franceschi, S., Lessi, F., Stefano, A. L. D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56827-5 · Cited by 40

Semisupervised Few-Shot Remote Sensing Image Classification Based on KNN Distance Entropy

Chao, X., & Li, Y. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3213749 · Cited by 38

Machine learning to empower electrohydrodynamic processing

Wang, F., Elbadawi, M., Tsilova, S. L., Gaisford, S., Basit, A. W., & Parhizkar, M. (2021) · Materials Science and Engineering C · doi:10.1016/j.msec.2021.112553 · Cited by 38

Recent advances and applications of machine learning in electrocatalysis

Hu, Y., Chen, J., Wei, Z. X., He, Q., & Zhao, Y. (2023) · Journal of Materials Informatics · doi:10.20517/jmi.2023.23 · Cited by 38

A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications

Shen, B., Feng, F., Li, K., Lin, P., Ma, L., & Li, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac605 · Cited by 31

Computational estimation of quality and clinical relevance of cancer cell lines

Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31

Few-shot learning with transformers via graph embeddings for molecular property prediction

Torres, L. H. M., Ribeiro, B., & Arrais, J. P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120005 · Cited by 32

Artificial intelligence in food and nutrition evidence: The challenges and opportunities

Bailey, R. L., MacFarlane, A. J., Field, M. S., Tagkopoulos, I., Baranzini, S. E., Edwards, K. M., Rose, C. J., Schork, N. J., Singhal, A., Wallace, B., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae461 · Cited by 34

Industrializing AI/ML during the end-to-end drug discovery process

Yoo, J., Kim, T. Y., Joung, I., & Song, S. O. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102528 · Cited by 29

Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

Siwek, J. C., Omelchenko, A. A., Chhibbar, P., Arshad, S., Rosengart, A., Nazarali, I., Patel, A., Nazarali, K., Rahimikollu, J., Tilstra, J. S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02723-1 · Cited by 27

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

Wang, Y., Yu, X., Gu, Y., Li, W., Zhu, K., Chen, L., Tang, Y., & Liu, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107746 · Cited by 26

FragNet, a Contrastive Learning-Based Transformer Model for Clustering, Interpreting, Visualizing, and Navigating Chemical Space

Shrivastava, A. D., & Kell, D. B. (2021) · Molecules · doi:10.3390/molecules26072065 · Cited by 25

DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques

Singh, D. P., Gupta, A. K., & Kaushik, B. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104562 · Cited by 25

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling

Ren, Q., Qu, N., Sun, J., Zhou, J., Liu, J., Ni, L., Tong, X., Zhang, Z., Kong, X., Wen, Y., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad461 · Cited by 24

Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine

Artsi, Y., Sorin, V., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13206246 · Cited by 23

Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework

Nayem, J., Hasan, S. S., Amina, N., Das, B., Ali, M. S., Ahsan, M. M., & Raman, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-47772-0_4 · Cited by 22

SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug DesignPreprint

Edwards, C., Naik, A., Khot, T., Burke, M. D., Ji, H., & Hope, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.06.547759 · Cited by 27

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

Demirel, H. C., Arici, M. K., & Tunçbağ, N. (2021) · Molecular Omics · doi:10.1039/d1mo00158b · Cited by 29

Routine omics collection is a golden opportunity for European human research in space and analog environments

Cope, H., Willis, C. R. G., MacKay, M. J., Rutter, L. A., Toh, L. S., Williams, P. M., Herranz, R., Borg, J., Bezdan, D., Giacomello, S., et al. (2022) · Patterns · doi:10.1016/j.patter.2022.100550 · Cited by 21

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia

Kong, W., He, L., Zhu, J., Brück, O. E., Porkka, K., Heckman, C. A., Zhu, S., & Aittokallio, T. A. (2022) · Leukemia · doi:10.1038/s41375-022-01662-6 · Cited by 21

End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins

Cai, T., Xie, L., Zhang, S., Chen, M., He, D., Badkul, A., Liu, Y., Krishna, N. H., Dorogan, M., Harding, W. W., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010851 · Cited by 20

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture

Zhang, Q., Zhang, S., Feng, Y.-H., & Shi, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3248041 · Cited by 21

A hierarchical attention network integrating multi-scale relationship for drug response prediction

Wang, X., Wen, Y., Zhang, Y., Dai, C., Yang, Y., Bo, X., He, S., & Peng, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102485 · Cited by 22

Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing

Park, Y., Hauschild, A.-C., & Heider, D. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab104 · Cited by 22

MIRACUM-Pipe: An Adaptable Pipeline for Next-Generation Sequencing Analysis, Reporting, and Visualization for Clinical Decision Making

Metzger, P., Hess, M. E., Blaumeiser, A., Pauli, T., Schipperges, V., Mertes, R., Christoph, J., Unberath, P., Reimer, N., Scheible, R., et al. (2023) · Cancers · doi:10.3390/cancers15133456 · Cited by 19

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Hajim, W. I., Zainudin, S., Daud, K. M., & Alheeti, K. M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1903 · Cited by 19

CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction

Singh, D. P., & Kaushik, B. (2023) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2023.107868 · Cited by 18

Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome

Huang, K., & Liu, H. (2025) · Communications Biology · doi:10.1038/s42003-025-07959-3 · Cited by 19

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

Veríssimo, G. C., Serafim, M. S. M., Kronenberger, T., Ferreira, R. S., Honório, K. M., & Maltarollo, V. G. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2114451 · Cited by 17

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks

Liu, H., Peng, W., Dai, W., Lin, J., Fu, X., Liu, L., Liu, L., & Yu, N. (2023) · Methods · doi:10.1016/j.ymeth.2023.11.018 · Cited by 16

Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond

Gao, Y., Wei, Z., Dong, K., Chen, K., Yang, J., Chuai, G., & Liu, Q. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00698-1 · Cited by 18

The Hallmarks of Predictive Oncology

Singhal, A., Zhao, X., Wall, P., So, E., Calderini, G., Partin, A., Koussa, N. C., Vasanthakumari, P., Narykov, O., Zhu, Y., et al. (2025) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0760 · Cited by 19

Discovery of predictive biomarkers for cancer therapy through computational approaches

Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 24

Exploring new horizons: Empowering computer-assisted drug design with few-shot learning

Mendonça, S. S., Vitória, A. R. S., de Lima, T. W., Filho, A. R. G., & Andrade, C. H. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100086 · Cited by 16

Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data

Gross, B., Dauvin, A., Cabeli, V., Kmetzsch, V., Khoury, J. E., Dissez, G., Ouardini, K., Grouard, S., Davi, A., Loeb, R., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-67023-8 · Cited by 16

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell LinesPreprint

Clark, T., Mohan, J., Schaffer, L. V., Obernier, K., Manir, S. A., Churas, C., Dailamy, A., Doctor, Y., Forget, A., Hansen, J. N., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.21.589311 · Cited by 19

Applications of big data in clinical applications

Pramanik, S., Obaid, A. J., Niranjanamurthy, M., & Bandyopadhyay, S. K. (2023) · AIP conference proceedings · doi:10.1063/5.0119414 · Cited by 13

Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction

Torres, L. H. M., Arrais, J. P., & Ribeiro, B. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00902-4 · Cited by 14

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

Ramon, A., Ni, M., Predeina, O., Gaffey, R., Kunz, P., Onuoha, S., & Sormanni, P. (2025) · mAbs · doi:10.1080/19420862.2024.2442750 · Cited by 13

Meta-learning reduces the amount of data needed to build AI models in oncology

Gevaert, O. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01358-1 · Cited by 13

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment

Liu, Z., Chen, R., Yang, L., Jiang, J., Ma, S., Chen, L., He, M., Mao, Y., Guo, C., Kong, X., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad888 · Cited by 12

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Cadavid, J. L., Li, N. T., & McGuigan, A. P. (2024) · Biophysics Reviews · doi:10.1063/5.0179125 · Cited by 11

One-shot skill assessment in high-stakes domains with limited data via meta learning

Yanik, E., Schwaitzberg, S., Yang, G., Intes, X., Norfleet, J., Hackett, M., & De, S. (2022) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2024.108470 · Cited by 12

Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

Torres, L. H. M., Arrais, J. P., & Ribeiro, B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08403-5 · Cited by 11

Towards an interpretable deep learning model of cancer

Nilsson, A., Meimetis, N., & Lauffenburger, D. A. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00822-y · Cited by 11

A meta-learning approach to improving radiation response prediction in cancers

Zhang, Y.-N., Qiu, L., Ren, Y.-Y., Cheng, Z., Li, L., Yao, S.-Q., Zhang, C.-D., Luo, Z., & Lu, H. (2022) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2022.106163 · Cited by 8

Spectroscopy-Guided Deep Learning Predicts Solid–Liquid Surface Adsorbate Properties in Unseen Solvents

Du, W., Ma, F., Zhang, B., Zhang, J., Wu, D., Sharman, E. H., Jiang, J., & Wang, Y. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c10921 · Cited by 10

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

Luo, X., Ding, Y., Cao, Y., Liu, Z., Zhang, W., Zeng, S., Cheng, S. H., Li, H., Haggarty, S. J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110875 · Cited by 10

Growth‐rate model predicts in vivo tumor response from in vitro data

Diegmiller, R., Salphati, L., Alicke, B., Wilson, T. R., Stout, T. J., & Hafner, M. A. (2022) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12836 · Cited by 10

Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens

Hassan, A. Z., Ward, H. N., Rahman, M., Billmann, M., Lee, Y., & Myers, C. L. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311657 · Cited by 7

Exploring Few-Shot Learning Approaches for Bioinformatics Advancements

Bhati, N., Duggar, R., & Alzahrani, A. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-1822-5.ch016 · Cited by 9

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Njock, P. G. A., Yin, Z., & Zhang, N. (2024) · International Journal for Numerical and Analytical Methods in Geomechanics · doi:10.1002/nag.3862 · Cited by 9

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Hasan, K. R., Kim, S., Cho, J., & Han, H. S. (2025) · IEEE Access · doi:10.1109/access.2025.3570673 · Cited by 9

MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling

Wang, K., Li, B., Xu, M., Ding, D., Zheng, Q., Tian, G., Zeng, X., & Yang, J. (2025) · BMC Biology · doi:10.1186/s12915-025-02457-8 · Cited by 9

Modeling breast cancer proliferation, drug synergies, and alternating therapies

He, W., Demas, D. M., Shajahan‐Haq, A. N., & Baumann, W. T. (2023) · iScience · doi:10.1016/j.isci.2023.106714 · Cited by 8

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Prasse, P., Iversen, P., Lienhard, M., Thedinga, K., Herwig, R., & Scheffer, T. (2022) · Cancers · doi:10.3390/cancers14163950 · Cited by 9

Harmonizing across datasets to improve the transferability of drug combination prediction

Zhang, H., Wang, Z., Nan, Y., Zagidullin, B., Yi, D., Tang, J. P., & Guan, Y. (2023) · Communications Biology · doi:10.1038/s42003-023-04783-5 · Cited by 8

SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations

Boonyarit, B., Kositchutima, M., Phattalung, T. N., Yamprasert, N., Thuwajit, C., Rungrotmongkol, T., & Nutanong, S. Y. (2025) · GigaScience · doi:10.1093/gigascience/giaf080 · Cited by 7

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

Shen, J. P. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210212 · Cited by 9

Flexible few-shot class-incremental learning with prototype container

Xu, X., Wang, Z., Fu, Z., Guo, W., Chi, Z., & Li, D. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08272-y · Cited by 8

Drug response in the era of precision medicine: A methodological review

Okyere, D., Bravo-Merodio, L., Xu, Y., Guan, X., Parkhi, D., Gkoutos, G. V., & Acharjee, A. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.11.067 · Cited by 9

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis

Partin, A., Vasanthakumari, P., Narykov, O., Wilke, A., Koussa, N. C., Jones, S. E., Zhu, Y., Overbeek, J., Jain, R., Fernando, G. D., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf667 · Cited by 12

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Peng, W., Chen, C., Dai, W., Yu, N., & Wang, J. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3462811 · Cited by 7

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Dong, L. F., Qian, Y., Gonzalez, P., Öz, O. K., & Sun, X. (2023) · ACM SIGAPP Applied Computing Review · doi:10.1145/3594264.3594267 · Cited by 7

Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples

So, E., Yu, F., Wang, B., & Haibe‐Kains, B. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00688-4 · Cited by 7

Reusability Report: Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patientsPreprint

So, E., Yu, F., Wang, B., & Haibe‐Kains, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.06.547938

Abstract 1159: Predicting clinical drug responses using a few-shot learning-based interpretable AI

Park, S., Singhal, A., Silva, E., Kreisberg, J. F., & Ideker, T. E. (2022) · Cancer Research · doi:10.1158/1538-7445.am2022-1159 · Cited by 1

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Wang, Y., Liu, X., Fan, Y., Xie, B., Cheng, J. S.-C., Wong, K. C., Cheung, P. P., King, I., & Li, Y. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00887-6 · Cited by 14

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Ge, S., Sun, S., Ren, Y., Xu, H. Y., Cheng, Q. S., & Ren, Z. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.25.591050

Sample-Efficient Adaptation of Drug-Response Models to Patient Tumors under Strong Biological Domain ShiftPreprint

Cortes, C. J., Lalanda, P., & Vega, G. (2026) · arXiv.org · doi:10.48550/arxiv.2603.16185