We show that under rather general circumstances, the almost everywhere
pointwise inequality $|f|(x) \le Mf (x)$ is equivalent to a weak form of the
Lebesgue density theorem, for totally bounded closed sets. We derive both
positive and negative results from this characterization.Comment: 13 pages, incorporates suggestions by a refere

Alexios Koutsoukas (1773175)

Jun Huan (2188498)

Keith Monaghan (4181548)

Xiaoli Li (279663)

Journal of Cheminformatics

English

arXiv

10.1186/s13321-017-0226-yJournal of Cheminformatics914

Koutsoukas, A

Monaghan, K.J

Li, X

Huan, J

ScholarBank@NUS

Deep-learning: Investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Additional file 3. Results of robustness testing

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Abstract Background In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. Results We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets

Alexios Koutsoukas

Keith J. Monaghan

Xiaoli Li

Jun Huan

Directory of Open Access Journals

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Additional file 2. Code developed in this project

MOESM2 of Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Additional file 4. Results of parameter selection

MOESM4 of Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

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On the pointwise domination of a function by its maximal function

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