Molecular data representation based on gene embeddings for cancer drug response prediction

Park, Sejin; Lee, Hyunju

doi:10.1038/s41598-023-49003-6

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Unlike the previous methods that use a fixed set of genes for all cell lines, GEN 58 utilizes individualized set of genes for each cell line. Additionally, GEN learns gene embedding vectors, potentially enabling informative representation of cell lines, which could improve ranking performance compared to DeepCDR and GraTransDRP.…”

Section: Methodsmentioning

confidence: 99%

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank

Dey,

Ning

2024

J. Chem. Inf. Model.

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Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug–cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most effective drugs for each cell line still remains a significant challenge. To address this, we developed multiple neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed multiple pairwise and listwise neural 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. Specifically, we developed neural pairwise and listwise ranking methods, Pair-PushC and List-One on top of the existing methods, pLETORg and ListNet, respectively. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the effective drugs instead of the top effective drug, unlike List-One. We also provide an exhaustive empirical evaluation with state-of-the-art regression and ranking baselines on large-scale data sets across multiple experimental settings. Our results demonstrate that our proposed ranking methods mostly outperform the best baselines with significant improvements of as much as 25.6% in terms of selecting truly effective drugs within the top 20 predicted drugs (i.e., hit@20) across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).

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Section: Methodsmentioning

confidence: 99%

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank

Dey,

Ning

2024

J. Chem. Inf. Model.

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Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Park,

Lee

2024

Briefings in Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomic analysis due to information loss. To address this limitation, we present scRobust, a robust self-supervised learning strategy to tackle the inherent sparsity of scRNA-seq data. Built upon the Transformer architecture, scRobust employs a novel self-supervised learning strategy comprising contrastive learning and gene expression prediction tasks. We demonstrated the effectiveness of scRobust using nine benchmarks, additional dropout scenarios, and combined datasets. scRobust outperformed recent methods in cell-type annotation tasks and generated cell embeddings that capture multi-faceted clustering information (e.g. cell types and HbA1c levels). In addition, cell embeddings of scRobust were useful for detecting specific marker genes related to drug tolerance stages. Furthermore, when we applied scRobust to scATAC-seq data, high-quality cell embedding vectors were generated. These results demonstrate the representational power of scRobust.

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Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency

Baek,

Lee

2024

BMC Bioinformatics

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Molecular data representation based on gene embeddings for cancer drug response prediction

Cited by 3 publications

References 38 publications

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency

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