dimRed and coRanking - Unifying Dimensionality Reduction in R

Kraemer, Guido; Reichstein, Markus; Mahecha, Miguel D.

doi:10.32614/rj-2018-039

Cited by 53 publications

(54 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised Supervised Software Autoencoder Keras (Chollet, 2015), R: dimRed (Kraemer et al, 2018), h2o (Candel et al, 2015), RcppDL (Kou and Sugomori, 2014) Convolutional Deep Belief Network (CDBN) R & python: TensorFlow (Abadi et al, 2016), Keras (Chollet, 2015), h2o (Candel et al, 2015) Convolutional Neural Network (CNN) R & python: Keras (Chollet, 2015) MXNet (Chen et al, 2015), Tensorflow (Abadi et al, 2016), h2O (Candel et al, 2015), fastai (python) (Howard and Gugger, 2018) Deep Belief Network (DBN) RcppDL (R) (Kou and Sugomori, 2014), python: Caffee (Jia et al, 2014), Theano (Theano Development Team, 2016), Pytorch (Paszke et al, 2017), R & python: TensorFlow (Abadi et al, 2016), h2O (Candel et al, 2015) Deep Boltzmann Machine (DBM) python: boltzmann-machines (Bondarenko, 2017), pydbm (Chimera, 2019)…”

Section: Modelmentioning

confidence: 99%

An Introductory Review of Deep Learning for Prediction Models With Big Data

Emmert‐Streib

Yang

Han

et al. 2020

Front. Artif. Intell.

433

210

View full text Add to dashboard Cite

Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly-in an almost Lego-like manner-to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.

show abstract

Section: Modelmentioning

confidence: 99%

An Introductory Review of Deep Learning for Prediction Models With Big Data

Emmert‐Streib

Yang

Han

et al. 2020

Front. Artif. Intell.

433

210

View full text Add to dashboard Cite

show abstract

“…We evaluated these techniques with HemoPI-1 model dataset using R NX (K) quality curves 50 (Fig. S2) using the dimRed R package 51 . Among the different techniques, we selected t-distributed stochastic neighbour embedding (t-SNE) to display HemoPI-1, AMP and HAMP datasets.…”

mentioning

confidence: 99%

Machine learning-guided discovery and design of non-hemolytic peptides

Plisson¹,

Ramírez-Sánchez²,

Martínez-Hernández³

2020

Sci Rep

107

104

View full text Add to dashboard Cite

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

show abstract

“…In particular: nonlinear methods typically require tuning of specific parameters, objective criteria are often lacking, a proper weighting of observations is difficult, and it is harder to interpret the resulting indicators due to their nonlinear nature (Kraemer et al, 2018). The salient feature of PCA is that an inverse projection is well defined and allows for a deeper inspection of the errors, which is not the case for nonlinear methods due to the pre-imaging problem (Mika et al, 1999;Arenas-Garcia et al, 2013).…”

mentioning

confidence: 99%

Summarizing the state of the terrestrial biosphere in few dimensions

Kraemer¹,

Camps‐Valls²,

Reichstein³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> In times of global change, we must closely monitor the state of the planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems &#8211; i.e. the biosphere, atmosphere, hydrosphere, and cryosphere &#8211; can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region. Although such approaches are also used in other fields of science, they are rarely used to describe land surface dynamics. Here, we propose a robust method to create indicators for the terrestrial biosphere using principal component analysis based on a high-dimensional set of relevant global data streams. The concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73&#8201;% of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions. We assume that this generic approach has great potential for the analysis of land-surface dynamics from observational or model data.</p>

show abstract

dimRed and coRanking - Unifying Dimensionality Reduction in R

Cited by 53 publications

References 30 publications

An Introductory Review of Deep Learning for Prediction Models With Big Data

An Introductory Review of Deep Learning for Prediction Models With Big Data

Machine learning-guided discovery and design of non-hemolytic peptides

Summarizing the state of the terrestrial biosphere in few dimensions

Contact Info

Product

Resources

About