ArticleBioinformatics (Oxford, England)2020
AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- MDCDR: predicting cancer drug response via multimodal feature fusion and feature disentanglement.Briefings in bioinformatics · 2026Article
- Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis.Briefings in bioinformatics · 2026Article
- A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts.NAR genomics and bioinformatics · 2025Article
- Article
- A disentangled generative model for improved drug response prediction in patients via sample synthesis.Journal of pharmaceutical analysis · 2025Article
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis.Health data science · 2025Review
- Domain adaptation in small-scale and heterogeneous biological datasets.Science advances · 2024Review
- Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.Statistics in biosciences · 2024Article
- Evaluating large language models for annotating proteins.Briefings in bioinformatics · 2024Article
- PANCDR: precise medicine prediction using an adversarial network for cancer drug response.Briefings in bioinformatics · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.Genomics, proteomics & bioinformatics · 2023Article
- Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- CREAMMIST: an integrative probabilistic database for cancer drug response prediction.Nucleic acids research · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Feature selection translates drug response predictors from cell lines to patients.Frontiers in genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution.
resultsWe propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately. AVAILABILITY AND IMPLEMENTATION: https://github.com/hosseinshn/AITL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.