WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer

• URL: http://arxiv.org/abs/2405.04078v1
• Date: Tue, 7 May 2024 07:21:20 GMT
• Title: WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer
• Authors: Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan,
• Abstract summary: Cancer is a leading cause of death globally due to genomic changes and manifests heterogeneously across patients. variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second.
• Score: 7.9505208530650995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (`cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method (WISER) over state-of-the-art alternatives on predicting personalized drug response.

Related papers

• DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network [9.641021461914551]
  We propose a novel interpretable predictive model, DRExplainer, for drug response prediction. DRExplainer constructs a directed bipartite network integrating multi-omics profiles of cell lines, the chemical structure of drugs and known drug response. In computational experiments, DRExplainer outperforms state-of-the-art predictive methods and another graph-based explanation method.
  arXiv  Detail & Related papers  (2024-08-22T05:45:48Z)
• Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization [0.7070726553564699]
  We propose the use of contrastive learning to improve learned drug and cell line representations. We find that our learned representations exhibit a more balances reliance on drug- and cell line-derived features when making predictions.
  arXiv  Detail & Related papers  (2023-10-20T04:18:47Z)
• Precision Anti-Cancer Drug Selection via Neural Ranking [0.342658286826597]
  We propose two neural listwise ranking methods that learn latent representations of drugs and cell lines, and then use those representations to score drugs in each cell line via a learnable scoring function. Our results demonstrate that List-All outperforms the best baseline with significant improvements of as much as 8.6% in hit@20 across 50% test cell lines.
  arXiv  Detail & Related papers  (2023-06-30T16:23:25Z)
• Zero-shot causal learning [64.9368337542558]
  CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
  arXiv  Detail & Related papers  (2023-01-28T20:14:11Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Deep learning methods for drug response prediction in cancer: predominant and emerging trends [50.281853616905416]
  Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
  arXiv  Detail & Related papers  (2022-11-18T03:26:31Z)
• TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response [12.86640026993276]
  We proposeTransformer based network for Cancer drug Response (TCR) to predict anti-cancer drug response. By utilizing an attention mechanism, TCR is able to learn the interactions between drug atom/sub-structure and molecular signatures efficiently. Our study highlights the prediction power of TCR and its potential value for cancer drug repurpose and precision oncology treatment.
  arXiv  Detail & Related papers  (2022-07-10T13:01:54Z)
• Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
  Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
  arXiv  Detail & Related papers  (2022-02-08T09:42:08Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.