Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Wang, Lin; Li, Xiaozhong; Zhang, Louxin; Gao, Qiang

doi:10.1186/s12885-017-3500-5

Cited by 150 publications

(151 citation statements)

References 32 publications

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“…The sensitivity of the cell line to the anticancer drug between the observed and the predicted was approximately 96.0. Comparing with the previous SRMF model in [19], which at the best was 0.71 on its data by = 0, was about 0.25. For SRMF model on data was about 0.95 Figure 1.1 compares the mean PCC_S / R between a samples of drugs, and averaged RMSE_S /R (root mean square error) between observed and predicted cell lines response to drugs in DSRMF model was 0.30, and in SRMF model was about 0.31 for data in this Research.…”

Section: Measurements Of Prediction Performancementioning

confidence: 57%

Improving Drug Response Prediction Using Dual Similarity Regularization

Ebadi

Soleimani

Ghaderzadeh

2020

Preprint

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Background:Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors.Results:We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug, Conclusions:Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.

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Section: Measurements Of Prediction Performancementioning

confidence: 57%

Improving Drug Response Prediction Using Dual Similarity Regularization

Ebadi

Soleimani

Ghaderzadeh

2020

Preprint

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“…Recently, many methods have been developed to predict the response of multiple drugs in a single model [11,17,[19][20][21]26]. In this so-called multi-task learning framework [31], sharing information on different drugs (tasks) often enables us to obtain a prediction method with high accuracy.…”

Section: Single-task Vs Multi-task Learningmentioning

confidence: 99%

“…The similarity-regularized matrix factorization (SRMF) method This method simply assumes that the drug response matrix R n×m is approximately a product of two low-rank matrices U n×k (a compression of the cell line profile matrix) and V m×k (a compression of the drug profile matrix). Wang et al used a cell line similarity matrix S cell and a drug similarity matrix S drug to compute the two low-rank matrices [21] as follows:…”

Section: Dualnetsmentioning

confidence: 99%

“…For each group, MVLR learnt a model and made prediction. The drug groups are indexed alphabetically: ABL signaling (1), Apoptosis regulation (2), Cell cycle (3), Chromatin histone acetylation (4), Chromatin histone methylation (5), Chromatin other (6), Cytoskeleton(7), DNA replication (8), EGFR signaling (9), ERK MAPK signaling (10), Genome integrity (11), Hormone-related (12), IGFR signaling (13), JNK and p38 signaling(14), Metabolism(15), Mitosis(16), other drugs(17), Kinases (18), P53 pathway(19), PI3K/MTOR signaling(20), Protein stability and degradation(21), RTK signaling(22), WNT signaling(23).…”

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A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

Chen

Zhang

2019

Preprint

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Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancer and other diseases. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to solve drug response prediction problems. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assessed 17 representative methods for drug response prediction, which have been developed in the past five years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

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“…Many computational models predict drug response using gene expression profiles and drug molecular data [9][10][11] . Wang et al designed a machine learning model based on the observation that cell lines with similar gene expression patterns, or drugs with similar chemical properties, will exhibit similar responses 12 .…”

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confidence: 99%

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance

Choi

Park

Ahn

2020

Sci Rep

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Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine learning models have been developed for prediction of drug responses using various genomic data and diverse drug molecular information, but those methods are ineffective to predict drug response to untrained drugs and gene expression patterns, which is known as the cold-start problem. In this study, we present a novel deep neural network model, termed RefDNN, for improved prediction of drug resistance and identification of biomarkers related to drug response. RefDNN exploits a collection of drugs, called reference drugs, to learn representations for a high-dimensional gene expression vector and a molecular structure vector of a drug and predicts drug response labels using the reference drug-based representations. These calculations come from the observation that similar chemicals have similar effects. The proposed model not only outperformed existing computational prediction models in most comparative experiments, but also showed more robust prediction for untrained drugs and cancer types than traditional machine learning models. RefDNN exploits the ElasticNet regularization to deal with high-dimensional gene expression data, which allows identification of gene markers associated with drug resistance. Lastly, we described an application of RefDNN in exploring a new candidate drug for liver cancer. As the proposed model can guarantee good prediction of drug responses to untrained drugs for given gene expression patterns, it may be of potential benefit in drug repositioning and personalized medicine.

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Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Cited by 150 publications

References 32 publications

Improving Drug Response Prediction Using Dual Similarity Regularization

Improving Drug Response Prediction Using Dual Similarity Regularization

A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance

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