Boosting – An Unusual Yet Attractive Optimiser

2014 ◽  
Vol 53 (06) ◽  
pp. 417-418 ◽  
Author(s):  
T. Hothorn
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Statistical Modelling ◽  
Discussion Section ◽  
Boosting Algorithms

SummaryThis editorial is part of a For-Discussion- Section of Methods of Information in Medicine about the papers “The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling” [1] and “Ex-tending Statistical Boosting – An Overview of Recent Methodological Developments” [2], written by Andreas Mayr and co authors. It preludes two discussed reviews on developments and applications of boosting in biomedical research. The two review papers, written by Andreas Mayr, Harald Binder, Olaf Gefeller, and Matthias Schmid, give an overview on recently published methods that utilise gradient or likelihood-based boosting for fitting models in the life sciences. The reviews are followed by invited comments [3] by experts in both boosting theory and applications.

Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”

2014 ◽  
Vol 53 (06) ◽  
pp. 436-445 ◽  
Author(s):  
P. Bühlmann ◽  
J. Gertheiss ◽  
S. Hieke ◽  
T. Kneib ◽  
S. Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Modelling ◽  
Letters To The Editor ◽  
Discussion Section ◽  
Boosting Algorithms

SummaryThis article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers “The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling” [1] and “Extending Statistical Boosting – An Overview of Recent Methodological Developments” [2], written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In sub-sequent issues the discussion can continue through letters to the editor.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

scholarly journals The Evolution of Boosting Algorithms

2014 ◽  
Vol 53 (06) ◽  
pp. 419-427 ◽  
Author(s):  
H. Binder ◽  
O. Gefeller ◽  
M. Schmid ◽  
A. Mayr
Keyword(s):  
Machine Learning ◽  
Statistical Modelling ◽  
General Concept ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Statistical Regression ◽  
Adaboost Algorithm ◽  
Software Implementations ◽  
Boosting Algorithms ◽  
Substantial Interest

SummaryBackground: The concept of boosting emerged from the field of machine learning. The basic idea is to boost the accuracy of a weak classifying tool by combining various instances into a more accurate prediction. This general concept was later adapted to the field of statistical modelling. Nowadays, boosting algorithms are often applied to estimate and select predictor effects in statistical regression models.Objectives: This review article attempts to highlight the evolution of boosting algorithms from machine learning to statistical modelling.Methods: We describe the AdaBoost algorithm for classification as well as the two most prominent statistical boosting approaches, gradient boosting and likelihood-based boosting for statistical modelling. We highlight the methodological background and present the most common software implementations.Results: Although gradient boosting and likelihood-based boosting are typically treated separately in the literature, they share the same methodological roots and follow the same fundamental concepts. Compared to the initial machine learning algorithms, which must be seen as black-box prediction schemes, they result in statistical models with a straight-forward interpretation.Conclusions: Statistical boosting algorithms have gained substantial interest during the last decade and offer a variety of options to address important research questions in modern biomedicine.

scholarly journals Boosting for statistical modelling-A non-technical introduction

2018 ◽  
Vol 18 (3-4) ◽  
pp. 365-384 ◽  
Author(s):  
Andreas Mayr ◽  
Benjamin Hofner
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Statistical Models ◽  
Statistical Modelling ◽  
High Dimensional ◽  
Parametric Modelling ◽  
Breast Cancer Patients ◽  
Boosting Algorithms ◽  
Practical Implications

Boosting algorithms were originally developed for machine learning but were later adapted to estimate statistical models—offering various practical advantages such as automated variable selection and implicit regularization of effect estimates. The interpretation of the resulting models, however, remains the same as if they had been fitted by classical methods. Boosting, hence, allows to use an advanced machine learning scheme to estimate various types of statistical models. This tutorial aims to highlight how boosting can be used for semi-parametric modelling, what practical implications follow from the design of the algorithm and what kind of drawbacks data analysts have to expect. We illustrate the application of boosting in the analysis of a stunting score from children in India and a high-dimensional dataset of tumour DNA to develop a biomarker for the occurrence of metastases in breast cancer patients.

scholarly journals Extending Statistical Boosting

2014 ◽  
Vol 53 (06) ◽  
pp. 428-435 ◽  
Author(s):  
H. Binder ◽  
O. Gefeller ◽  
M. Schmid ◽  
A. Mayr
Keyword(s):  
Variable Selection ◽  
Biomedical Research ◽  
Statistical Models ◽  
Statistical Modelling ◽  
Gradient Boosting ◽  
Model Choice ◽  
Unified Framework ◽  
Different Types ◽  
Boosting Algorithms ◽  
Substantial Interest

SummaryBackground: Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade.Objectives: This review highlights recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research.Methods: We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed separately in the literature up to now.Results: The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: i) efforts to ensure variable selection leading to sparser models, ii) developments regarding different types of predictor effects and how to choose them, iii) approaches to extend the statistical boosting framework to new regression settings.Conclusions: Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any regression setting in combination with a large amount of different types of predictor effects.

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Machine learning in the life sciences

2007 ◽  
Vol 26 (2) ◽  
pp. 14-16 ◽  
Author(s):  
Krzysztof J. Cios ◽  
Lukasz A. Kurgan ◽  
Marek Reformat
Keyword(s):  
Machine Learning ◽  
Life Sciences

scholarly journals Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Patrick S. Stumpf ◽  
Xin Du ◽  
Haruka Imanishi ◽  
Yuya Kunisaki ◽  
Yuichiro Semba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bone Marrow ◽  
Single Cell ◽  
Rna Sequencing ◽  
Transfer Learning ◽  
Biomedical Research ◽  
Human Cell ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

scholarly journals Multiplexed whole-animal imaging with reversibly switchable optoacoustic proteins

2020 ◽  
Vol 6 (24) ◽  
pp. eaaz6293 ◽  
Author(s):  
Kanuj Mishra ◽  
Mariia Stankevych ◽  
Juan Pablo Fuenzalida-Werner ◽  
Simon Grassmann ◽  
Vipul Gujrati ◽  
...  
Keyword(s):  
Machine Learning ◽  
T Lymphocytes ◽  
Life Sciences ◽  
Imaging Systems ◽  
Temporal Domain ◽  
Animal Imaging ◽  
Highly Sensitive

We introduce two photochromic proteins for cell-specific in vivo optoacoustic (OA) imaging with signal unmixing in the temporal domain. We show highly sensitive, multiplexed visualization of T lymphocytes, bacteria, and tumors in the mouse body and brain. We developed machine learning–based software for commercial imaging systems for temporal unmixed OA imaging, enabling its routine use in life sciences.

scholarly journals Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

2020 ◽  
Vol 3 (1) ◽  
pp. 61-87 ◽  
Author(s):  
Theodore Alexandrov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mass Spectrometry ◽  
Image Analysis ◽  
Imaging Mass Spectrometry ◽  
Rapid Evolution ◽  
Life Sciences ◽  
Imaging Data ◽  
Enabling Technology ◽  
Tissue Sections

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology—imaging mass spectrometry—generate big hyperspectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

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