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.

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.

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 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 Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2020 ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Recommender System ◽  
In Silico ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

SummaryWhile understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies.Graphical abstractHighlightsLarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomesCalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc)Prediction of drug response in patient-derived cell lines with transcriptomic heterogeneityIn silico identification of drug combinations that work based on clonal vulnerabilities

Challenge-Enabled Machine Learning to Drug-Response Prediction

2020 ◽  
Vol 22 (5) ◽  
Author(s):  
Ziyan Wang ◽  
Hongyang Li ◽  
Christopher Carpenter ◽  
Yuanfang Guan
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Response Prediction

scholarly journals Machine learning approaches to drug response prediction: challenges and recent progress

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
George Adam ◽  
Ladislav Rampášek ◽  
Zhaleh Safikhani ◽  
Petr Smirnov ◽  
Benjamin Haibe-Kains ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Response ◽  
Recent Progress ◽  
Response Prediction ◽  
Learning Approaches

scholarly journals Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression

2021 ◽  
Vol 19 (1) ◽  
pp. e10
Author(s):  
Kexin Qiu ◽  
JoongHo Lee ◽  
HanByeol Kim ◽  
Seokhyun Yoon ◽  
Keunsoo Kang
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cell Line ◽  
Cancer Cell ◽  
Drug Response ◽  
Cancer Cell Line ◽  
Response Prediction ◽  
Cancer Drug ◽  
Anti Cancer
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