scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquín Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

AbstractBackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban-Medina ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquin Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

Abstract Background In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. Results The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. Conclusions The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquin Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

Abstract Background: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. Results: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. Conclusions: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Leveraging TCGA gene expression data to build predictive models for cancer drug response

2020 ◽  
Vol 21 (S14) ◽  
Author(s):  
Evan A. Clayton ◽  
Toyya A. Pujol ◽  
John F. McDonald ◽  
Peng Qiu
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Predictive Models ◽  
Drug Response ◽  
Cancer Drug ◽  
Expression Data ◽  
Classification Methods ◽  
Clustering And Classification ◽  
Machine Learning Models

Abstract Background Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients’ primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. Results We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study’s limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. Conclusions Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

scholarly journals Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

2021 ◽  
Vol 12 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

scholarly journals Representation transfer for differentially private drug sensitivity prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i218-i224
Author(s):  
Teppo Niinimäki ◽  
Mikko A Heikkilä ◽  
Antti Honkela ◽  
Samuel Kaski
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Private Information ◽  
Drug Sensitivity ◽  
Genomic Data ◽  
Representation Learning ◽  
Compact Representation ◽  
Cancer Type ◽  
Learning Methods ◽  
Drug Sensitivity Prediction

Abstract Motivation Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymization strategies fail to provide sufficient level of protection for genomic data, because the data are inherently identifiable. Differentially private machine learning can help by guaranteeing that the published results do not leak too much information about any individual data point. Recent research has reached promising results on differentially private drug sensitivity prediction using gene expression data. Differentially private learning with genomic data is challenging because it is more difficult to guarantee privacy in high dimensions. Dimensionality reduction can help, but if the dimension reduction mapping is learned from the data, then it needs to be differentially private too, which can carry a significant privacy cost. Furthermore, the selection of any hyperparameters (such as the target dimensionality) needs to also avoid leaking private information. Results We study an approach that uses a large public dataset of similar type to learn a compact representation for differentially private learning. We compare three representation learning methods: variational autoencoders, principal component analysis and random projection. We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction. The experiments demonstrate significant benefit from all representation learning methods with variational autoencoders providing the most accurate predictions most often. Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction. Availability and implementation Code used in the experiments is available at https://github.com/DPBayes/dp-representation-transfer.

scholarly journals Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models (Preprint)

2020 ◽  
Author(s):  
Canelle Poirier ◽  
Dianbo Liu ◽  
Leonardo Clemente ◽  
Xiyu Ding ◽  
Matteo Chinazzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
News Media ◽  
Data Augmentation ◽  
Mechanistic Model ◽  
Digital Data ◽  
Mechanistic Models ◽  
Search Activity ◽  
Current Time ◽  
Aid Decision

BACKGROUND The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. OBJECTIVE We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. METHODS Our method uses the following as inputs: (a) official health reports, (b) COVID-19–related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. RESULTS Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. CONCLUSIONS Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

scholarly journals Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

10.2196/20285 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e20285
Author(s):  
Dianbo Liu ◽  
Leonardo Clemente ◽  
Canelle Poirier ◽  
Xiyu Ding ◽  
Matteo Chinazzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
News Media ◽  
Data Augmentation ◽  
Mechanistic Model ◽  
Digital Data ◽  
Mechanistic Models ◽  
Search Activity ◽  
Current Time ◽  
Aid Decision

Background The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. Objective We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Methods Our method uses the following as inputs: (a) official health reports, (b) COVID-19–related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Results Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Conclusions Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

scholarly journals Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu ◽  
Leonardo Clemente ◽  
Canelle Poirier ◽  
Xiyu Ding ◽  
Matteo Chinazzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
News Media ◽  
Data Augmentation ◽  
Mechanistic Model ◽  
Digital Data ◽  
Mechanistic Models ◽  
Search Activity ◽  
Current Time ◽  
Aid Decision

UNSTRUCTURED The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. Our method uses the following as inputs: (a) official health reports, (b) COVID-19–related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

scholarly journals Machine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer

2018 ◽  
Vol 17 ◽  
pp. 117693511881021 ◽  
Author(s):  
Melissa Zhao ◽  
Yushi Tang ◽  
Hyunkyung Kim ◽  
Kohei Hasegawa
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Predictive Models ◽  
Dimensional Reduction ◽  
Variable Importance ◽  
Classification Model ◽  
Support Vector ◽  
Knn Classification ◽  
Data Points

Objective: Despite existing prognostic markers, breast cancer prognosis remains a difficult subject due to the complex relationships between many contributing factors and survival. This study seeks to integrate multiple clinicopathological and genomic factors with dimensional reduction across machine learning algorithms to compare survival predictions. Methods: This is a secondary analysis of the data from a prospective cohort study of female patients with breast cancer enrolled in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We constructed a series of predictive models: ensemble models (Gradient Boosting and Random Forest), support vector machine (SVM), and artificial neural networks (ANN) for 5-year survival based on clinicopathological and gene expression data after K-means clustering with K-nearest-neighbor (KNN) classification. Model performance was evaluated by receiver operating characteristic (ROC) curve, accuracy, and calibration slope (CS). Model stability was assessed over 10 random runs in terms of ROC, accuracy, CS, and variable importance. Results: The analytic cohort is composed of 1874 patients with breast cancer. Overall, the median age was 62 years; the 5-year survival rate was 75%. ROC and accuracy were not significantly different between models (ROC and accuracy around 0.67 and 0.72 across models, respectively). However, ensemble methods resulted in better fit (CS) with stable measures of variable importance across 10 random training/validation splits. K-means clustering of gene expression profiles on training data points along with KNN classification of validation data points was a robust method of dimensional reduction. Furthermore, the gene expression cluster with the highest mortality risk was an influential factor in model prediction. Conclusions: Using machine learning methods to construct predictive models for 5-year survival in patients with breast cancer, we demonstrated discrimination ability across models with new insight into the stability and utility of dimensional reduction on genomic features in breast cancer survival prediction.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marina Esteban-Medina ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquín Dopazo
Keyword(s):  
Machine Learning ◽  
Fanconi Anemia ◽  
Mechanistic Models ◽  
Fanconi Anemia Pathway
Sign in / Sign up

Export Citation Format

Share Document