scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction

• URL: http://arxiv.org/abs/2505.05612v1
• Date: Thu, 08 May 2025 19:46:19 GMT
• Title: scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction
• Authors: Qing Wang, Yining Pan, Minghao Zhou, Zijia Tang, Yanfei Wang, Guangyu Wang, Qianqian Song,
• Abstract summary: scDrugMap is an integrated framework featuring both a Python command-line interface and a web server for drug response prediction.<n> scDrugMap evaluates a wide range of foundation models, including eight single-cell models and two large language models.<n> scDrugMap provides the first large-scale benchmark of foundation models for drug response prediction in single-cell data.
• Score: 5.058966163642362
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Drug resistance presents a major challenge in cancer therapy. Single cell profiling offers insights into cellular heterogeneity, yet the application of large-scale foundation models for predicting drug response in single cell data remains underexplored. To address this, we developed scDrugMap, an integrated framework featuring both a Python command-line interface and a web server for drug response prediction. scDrugMap evaluates a wide range of foundation models, including eight single-cell models and two large language models, using a curated dataset of over 326,000 cells in the primary collection and 18,800 cells in the validation set, spanning 36 datasets and diverse tissue and cancer types. We benchmarked model performance under pooled-data and cross-data evaluation settings, employing both layer freezing and Low-Rank Adaptation (LoRA) fine-tuning strategies. In the pooled-data scenario, scFoundation achieved the best performance, with mean F1 scores of 0.971 (layer freezing) and 0.947 (fine-tuning), outperforming the lowest-performing model by over 50%. In the cross-data setting, UCE excelled post fine-tuning (mean F1: 0.774), while scGPT led in zero-shot learning (mean F1: 0.858). Overall, scDrugMap provides the first large-scale benchmark of foundation models for drug response prediction in single-cell data and serves as a user-friendly, flexible platform for advancing drug discovery and translational research.

Related papers

• A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB) [0.0]
  Time series anomaly detection is critical for modern digital infrastructures.<n>We present a forecasting-based framework unifying classical methods with deep learning architectures.<n>We conduct the first complete evaluation on the Numenta Anomaly Benchmark.
  arXiv  Detail & Related papers  (2025-10-13T08:31:42Z)
• Evaluating New AI Cell Foundation Models on Challenging Kidney Pathology Cases Unaddressed by Previous Foundation Models [7.770106550946461]
  Accurate cell nuclei segmentation is critical for downstream tasks in kidney pathology.<n>We benchmarked advanced AI cell foundation models against three widely used cell foundation models developed prior to 2024.<n>CellViT++ [Virchow] yields the highest standalone performance with 40.3% of predictions rated as "Good" on a curated set of 2,091 challenging samples.
  arXiv  Detail & Related papers  (2025-10-01T00:38:36Z)
• Comprehensive Benchmarking of YOLOv11 Architectures for Scalable and Granular Peripheral Blood Cell Detection [7.1318006858231975]
  Manual peripheral blood smear (PBS) analysis is labor intensive and subjective.<n>While deep learning offers a promising alternative, a systematic evaluation of state of the art models such as YOLOv11 for fine grained PBS detection is still lacking.<n>We curate a large scale annotated dataset for blood cell detection and classification, comprising 16,891 images across 12 peripheral blood cell classes, along with the red blood cell class, all carefully re annotated for object detection tasks.<n>In total, the dataset contains 298,850 annotated cells.
  arXiv  Detail & Related papers  (2025-09-29T11:00:19Z)
• Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval [49.1574468325115]
  We introduce Amharic-specific dense retrieval models based on pre-trained Amharic BERT and RoBERTa backbones.<n>Our proposed RoBERTa-Base-Amharic-Embed model (110M parameters) achieves a 17.6% relative improvement in MRR@10.<n>More compact variants, such as RoBERTa-Medium-Amharic-Embed (42M) remain competitive while being over 13x smaller.
  arXiv  Detail & Related papers  (2025-05-25T23:06:20Z)
• DataDecide: How to Predict Best Pretraining Data with Small Experiments [67.95896457895404]
  We release models, data, and evaluations in DataDecide -- the most extensive open suite of models over differences in data and scale.<n>We conduct controlled pretraining experiments across 25 corpora with differing sources, deduplication, and filtering up to 100B tokens, model sizes up to 1B parameters, and 3 random seeds.
  arXiv  Detail & Related papers  (2025-04-15T17:02:15Z)
• A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction [6.199310532720352]
  We developed a multi-modal ensemble model (MMEM) that integrates clinical data, multi-omics data, and histopathology whole slide image (WSI) data.<n>MMEM predicted overall survival (OS) and disease-free survival (DFS) for ccRCC patients.
  arXiv  Detail & Related papers  (2024-12-10T02:51:14Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)
• Fast Polypharmacy Side Effect Prediction Using Tensor Factorisation [2.631060597686179]
  We demonstrate that tensor factorisation models can achieve state-of-the-art performance on polypharmacy side effect prediction.<n>Our best model (SimplE) achieves median scores of 0.978 AUROC, 0.971 AUPRC, and 1.000 AP@50 across 963 side effects.
  arXiv  Detail & Related papers  (2024-04-17T13:32:05Z)
• MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement [44.693325083735424]
  Tabular data prediction has been employed in medical applications such as patient health risk prediction. Previous predictors are often trained on manually curated small datasets.
  arXiv  Detail & Related papers  (2023-05-20T03:37:09Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• A Meta-GNN approach to personalized seizure detection and classification [53.906130332172324]
  We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
  arXiv  Detail & Related papers  (2022-11-01T14:12:58Z)
• An Interpretable Web-based Glioblastoma Multiforme Prognosis Prediction Tool using Random Forest Model [1.1024591739346292]
  We propose predictive models that estimate GBM patients' health status of one-year after treatments. We used total of 467 GBM patients' clinical profile consists of 13 features and two follow-up dates. Our machine learning models suggest that the top three prognostic factors for GBM patient survival were MGMT gene promoter, the extent of resection, and age.
  arXiv  Detail & Related papers  (2021-08-30T07:56:34Z)
• Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil [3.0711362702464675]
  The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. In this paper, autoregressive integrated moving average (ARIMA), cubist (CUBIST), random forest (RF), ridge regression (RIDGE), and stacking-ensemble learning are evaluated. The developed models can generate accurate forecasting, achieving errors in a range of 0.87% - 3.51%, 1.02% - 5.63%, and 0.95% - 6.90% in one, three, and six-days-ahead, respectively.
  arXiv  Detail & Related papers  (2020-07-21T17:58:58Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.