Stratification and prediction of drug synergy based on target functional similarity

Yang, Meizhu; Menden, Michael P.; Jaaks, Patricia; Dry, Jonathan R.; Garnett, Mathew J.; Sáez-Rodríguez, Julio

doi:10.1101/586123

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…The recently published NCI-ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) dataset [10] provides systematic evaluation of over 5,000 pairs of 104 FDA-approved anticancer drugs were scanned against a panel of 60 human tumor cell lines (NCI60) to discover those with enhanced growth inhibition or cytotoxicity profiles [32]. Combination activity was reported as a "ComboScore" that quantifies the advantage of combining two drugs which is a modified version of Bliss independence [11,37].…”

Section: Nci-almanacmentioning

confidence: 99%

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

Bazgir,

Ghosh,

Pal

2020

Preprint

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Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network) based models have shown promising results in drug sensitivity prediction. The primary idea behind REFINED CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from convolutional neural network architectures. However, the mapping from a vector to a compact 2D image is not unique due to variations in considered distance measures and neighborhoods. In this article, we consider predictions based on ensembles built from such mappings that can improve upon the best single REFINED CNN model prediction. Results illustrated using NCI60 and NCI-ALMANAC databases shows that the ensemble approaches can provide significant performance improvement as compared to individual models. We further illustrate that a single mapping created from the amalgamation of the different mappings can provide performance similar to stacking ensemble but with significantly lower computational complexity.

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Section: Nci-almanacmentioning

confidence: 99%

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

Bazgir,

Ghosh,

Pal

2020

Preprint

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“…(5)(6)(7) , and the Q g h is without best solution including Eqs. (8)(9)(10). For the diversity of the population to be balanced and to balance the convergence speed, two mutation operators are considered by the proposed technique.…”

Section: Proposed Techniquementioning

confidence: 99%

“…Taiji has been exhibited, software with a high-performance for quick and precise estimation of drug synergy dependent on the winning algorithm [9]. Also, synergy prediction work process can carry compound prioritization in huge scale medicate screenings, and synergy stratification work process can choose where the viability of medications definitely known for inciting synergy is higher [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Drug Synergy Prediction using Differential Evolution based Multinomial Random Forest

Kaur¹,

Singh²,

Kaur³

2019

IJACSA

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An efficient prediction of drug synergy plays a significant role in the medical domain. Examination of different drug-drug interaction can be achieved by considering the drug synergy score. With an rapid increase in cancer disease, it becomes difficult for doctors to predict significant amount of drug synergy. Because each cancer patient's infection level varies. Therefore, less or more amount of drug may harm these patients. Machine learning techniques are extensively used to estimate drug synergy score. However, machine learning based drug synergy prediction approaches suffer from the parameter tuning problem. To overcome this issue, in this paper, an efficient Differential evolution based multinomial random forest (DERF) is designed and implemented. Extensive experiments by considering the existing and the proposed DERF based machine learning models. The comparative analysis of DERF reveals that it outperforms existing techniques in terms of coefficient of determination, root mean squared error and accuracy.

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“…Various metrics of drug similarity have been proposed to represent drug pairs in drug synergy prediction models. The focus has primarily been on chemical features of drugs 17,[19][20][21] , and structural or network-level similarity of their targets within cells 9,[22][23][24] . Further quantities based on phenotypic effects of drugs such as therapeutic and side effect similarities or cell-line based sensitivity profiles have also been considered [25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

“…This is important, especially in precision medicine, where computational methods are required to enable accurate predictions in specific cellular contexts. Genome-wide expression profiles of cancer cell lines have been extensively employed for this purpose 17,18,20,23,[42][43][44][45] . However, the expression level of downstream genes is not necessarily a strong indicator of the functional status of the cell and may not directly connect with the drug response phenotype.…”

Section: Introductionmentioning

confidence: 99%

Predicting anti-cancer drug synergy using extended drug similarity profiles

Hosseini

Zhou

2022

Preprint

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Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much-needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context-aware and integrative deep learning framework that we have established to unleash the potential of the Chemical Checker extended drug similarity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context-generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.

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Stratification and prediction of drug synergy based on target functional similarity

Abstract: 3

Cited by 5 publications

References 33 publications

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction

A Novel Framework for Drug Synergy Prediction using Differential Evolution based Multinomial Random Forest

Predicting anti-cancer drug synergy using extended drug similarity profiles

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