scholarly journals Model-based tumor subclonal reconstruction

2019 ◽  
Author(s):  
Giulio Caravagna ◽  
Timon Heide ◽  
Marc Williams ◽  
Luis Zapata ◽  
Daniel Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Theory ◽  
Data Driven ◽  
Next Generation Sequencing Data ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Model Based ◽  
Novel Approach ◽  
History Of ◽  
Multiple Samples

AbstractThe vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Data-driven symbol detection via model-based machine learning

2020 ◽  
Vol 20 (3) ◽  
pp. 283-317
Author(s):  
Nariman Farsad ◽  
Nir Shlezinger ◽  
Andrea J. Goldsmith ◽  
Yonina C. Eldar
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
Model Based ◽  
Symbol Detection

scholarly journals Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2741 ◽  
Author(s):  
Miika J. Ahdesmäki ◽  
Simon R. Gray ◽  
Justin H. Johnson ◽  
Zhongwu Lai
Keyword(s):  
Open Source ◽  
Variant Calling ◽  
High Sensitivity ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Novel Approach ◽  
Gene Expression Quantification ◽  
Closed Source ◽  
Generation Sequencing ◽  
Bioinformatics Community

Grafting of cell lines and primary tumours is a crucial step in the drug development process between cell line studies and clinical trials. Disambiguate is a program for computationally separating the sequencing reads of two species derived from grafted samples. Disambiguate operates on alignments to the two species and separates the components at very high sensitivity and specificity as illustrated in artificially mixed human-mouse samples. This allows for maximum recovery of data from target tumours for more accurate variant calling and gene expression quantification. Given that no general use open source algorithm accessible to the bioinformatics community exists for the purposes of separating the two species data, the proposed Disambiguate tool presents a novel approach and improvement to performing sequence analysis of grafted samples. Both Python and C++ implementations are available and they are integrated into several open and closed source pipelines. Disambiguate is open source and is freely available at https://github.com/AstraZeneca-NGS/disambiguate.

scholarly journals Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

2019 ◽  
Vol 33 ◽  
pp. 2727-2735 ◽  
Author(s):  
Andreas Bunte ◽  
Benno Stein ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Quantitative Model ◽  
Machine Learning Algorithms ◽  
Seamless Integration ◽  
Model Based ◽  
Novel Approach ◽  
Qualitative Models ◽  
Easy Integration ◽  
Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

scholarly journals Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 561-569
Author(s):  
Matteo Taroni ◽  
Giorgio Vocalelli ◽  
Andrea De Polis
Keyword(s):  
B Value ◽  
Data Driven ◽  
Weighted Likelihood ◽  
High Definition ◽  
Seismic Catalogue ◽  
Novel Approach ◽  
Rolling Window ◽  
History Of ◽  
Likelihood Approach ◽  
Window Approach

We introduce a novel approach to estimate the temporal variation of the b-value parameter of the Gutenberg–Richter law, based on the weighted likelihood approach. This methodology allows estimating the b-value based on the full history of the available data, within a data-driven setting. We test this methodology against the classical “rolling window” approach using a high-definition Italian seismic catalogue as well as a global catalogue of high magnitudes. The weighted likelihood approach outperforms competing methods, and measures the optimal amount of past information relevant to the estimation.

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