scholarly journals An overview of machine learning methods for monotherapy drug response prediction

2021 ◽  
Author(s):  
Farzaneh Firoozbakht ◽  
Behnam Yousefi ◽  
Benno Schwikowski
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Response Prediction ◽  
Data Types ◽  
Basic Principles ◽  
Drug Responses ◽  
Machine Learning Methods ◽  
Fundamental Understanding ◽  
Biological Problem ◽  
Clinical Drug

Abstract For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

scholarly journals Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

2019 ◽  
Author(s):  
Edward W Huang ◽  
Ameya Bhope ◽  
Jing Lim ◽  
Saurabh Sinha ◽  
Amin Emad
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Large Databases ◽  
Tissue Of Origin ◽  
Molecular Profiles ◽  
Clinical Drug ◽  
Machine Learning Models

ABSTRACTPrediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue, but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients.We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples’ tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide significantly accurate prediction. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs’ mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.AUTHOR SUMMARYCancer is among the leading causes of death globally and perdition of the drug response of patients to different treatments based on their clinical and molecular profiles can enable individualized cancer medicine. Machine learning algorithms have the potential to play a significant role in this task; but, these algorithms are designed based the premise that a large number of labeled training samples are available, and these samples are accurate representation of the profiles of real tumors. However, due to ethical and technical reasons, it is not possible to screen humans for many drugs, significantly limiting the size of training data. To overcome this data scarcity problem, machine learning models can be trained using large databases of preclinical samples (e.g. cancer cell line cultures). However, due to the major differences between preclinical samples and real tumors, it is unclear how accurately such preclinical-to-clinical computational models can predict the clinical drug response of cancer patients.Here, first we systematically evaluate a variety of different linear and nonlinear machine learning algorithms for this particular task using two large databases of preclinical (GDSC) and tumor samples (TCGA). Then, we present a novel method called TG-LASSO that utilizes a new approach for explicitly incorporating the tissue of origin of samples in the prediction task. Our results show that TG-LASSO outperforms all other algorithms and can accurately distinguish resistant and sensitive patients for the majority of the tested drugs. Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity in each cancer type.

scholarly journals Drug Response Prediction as a Link Prediction Problem

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zachary Stanfield ◽  
Mustafa Coşkun ◽  
Mehmet Koyutürk
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Link Prediction ◽  
Drug Response ◽  
Response Prediction ◽  
Resistant Cell ◽  
Resistant Cell Line ◽  
Molecular Networks ◽  
Molecular Profile ◽  
Prediction Problem

Abstract Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.

Data in Construction Grammar

2013 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Linguistic ◽  
Statistical Analysis ◽  
Construction Grammar ◽  
Data Types ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
New Directions ◽  
Corpus Data

This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Grammar.

scholarly journals User Response Prediction in Online Advertising

2021 ◽  
Vol 54 (3) ◽  
pp. 1-43
Author(s):  
Zhabiz Gharibshah ◽  
Xingquan Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Apps ◽  
Online Advertising ◽  
Response Prediction ◽  
Online Marketing ◽  
Data Availability ◽  
Third Party ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Machine Learning Methods

Online advertising, as a vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How do we predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focusing on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open source codes in the field.

scholarly journals Predicting clinical drug response from model systems by non-linear subspace-based transfer learning

2020 ◽  
Author(s):  
Soufiane Mourragui ◽  
Marco Loog ◽  
Daniel J. Vis ◽  
Kat Moore ◽  
Anna G. Manjon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Response ◽  
Linear Manifold ◽  
Response Prediction ◽  
Human Tumors ◽  
Model Systems ◽  
Learning Approaches ◽  
Non Linear ◽  
Clinical Models ◽  
Clinical Drug

AbstractPre-clinical models have been the workhorse of cancer research for decades. While powerful, these models do not fully recapitulate the complexity of human tumors. Consequently, translating biomarkers of drug response from pre-clinical models to human tumors has been particularly challenging. To explicitly take these differences into account and enable an efficient exploitation of the vast pre-clinical drug response resources, we developed TRANSACT, a novel computational framework for clinical drug response prediction. First, TRANSACT employs non-linear manifold learning to capture biological processes active in pre-clinical models and human tumors. Then, TRANSACT builds predictors on cell line response only and transfers these to Patient-Derived Xenografts (PDXs) and human tumors. TRANSACT outperforms four competing approaches, including Deep Learning approaches, for a set of 15 drugs on PDXs, TCGA cohorts and 226 metastatic tumors from the Hartwig Medical Foundation data. For only four drugs Deep Learning outperforms TRANSACT. We further derived an algorithmic approach to interpret TRANSACT and used it to validate the approach by identifying known biomarkers to targeted therapies and we propose novel putative biomarkers of resistance to Paclitaxel and Gemcitabine.

scholarly journals Transferlernen in der Biomedizin

BIOspektrum ◽  
2020 ◽  
Vol 26 (6) ◽  
pp. 682-684
Author(s):  
Patrick Simon Stumpf ◽  
Lisa-Katrin Schätzle ◽  
Andreas Schuppert
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Biomedical Applications ◽  
Drug Response ◽  
Response Prediction ◽  
Model Systems ◽  
Complex Data ◽  
Learning Problem ◽  
Meaningful Information ◽  
Abundant Data

Abstract Machine learning is commonly employed to extract meaningful information from large and complex data. In situations where only scant data is available, algorithms can leverage abundant data from a separate (unrelated) context to address the learning problem. Here, we present two recently developed biomedical applications that take advantage of transfer learning to bridge the gap from model systems to human: single-cell label transfer and drug response prediction in patients.

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