An introduction to the maximum entropy approach and its application to inference problems in biology

Martino, Andrea De; Martino, Daniele De

doi:10.1016/j.heliyon.2018.e00596

Cited by 88 publications

(80 citation statements)

References 129 publications

(151 reference statements)

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“…It is clear that a layer only connects to the previous layer. FFNN applications is classified into two such as control of dynamical systems [ 32 , 33 ], and spaces where the classic machine learning techniques are applied [34] . NNs with two or more hidden layers are called deep networks because the network has become complex with more than 1 hidden layer.…”

Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,068

785

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This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

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Section: Main Textmentioning

confidence: 99%

State-of-the-art in artificial neural network applications: A survey

Abiodun

Omolara

Dada

et al. 2018

Heliyon

2,068

785

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“…As MDF turned out to be significantly worse than LSF (section ), the full details of the MDF method are given in the supporting information (section S2, derivation of the objective function, expressions for the gradient and Hessian), and here we give only the expression for the objective function minimized in MDF:

L = - \sum_{a} Z_{a} \sum_{i} q_{i}^{obs} ((), {normalP}_{a}) log ((), q_{i} ((), {normalP}_{a})) = - \sum_{a} Z_{a} q^{obs} ((), {normalP}_{a}) log ((), q^{T} ((), {normalP}_{a}))

where q (P a ) denotes the calculated quantity – be it v (P a ) or x (P a ), q obs (P a ) is the corresponding experimental quantity, and Z a represents the size of P a sample from which the experimental isotopic distribution is obtained (the number of detected P a molecules). Let us note that the objective function in the maximum entropy method is almost identical, only without the weighting factor Z a .…”

Section: Theorymentioning

confidence: 99%

Resolution of protein hydrogen/deuterium exchange by fitting amide exchange probabilities to the peptide isotopic envelopes

Babić

Kazazić

Smith

2019

Rapid Comm Mass Spectrometry

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Rationale: Mass spectra processing in protein hydrogen/deuterium (H/D) exchange has been remarkably improved by the introduction of fitting of the amide exchange probabilities to peptide isotopic envelope intensities (Kan et al., 2013), in contrast to methods in which only the peptide deuterium uptakes (centroid shifts of isotopic envelopes) are used. However, the known implementations are based on the general fitting routines that use only the objective function values. Besides, applicability of more than one fitting method makes necessary their comparative evaluation.Methods: Two fitting methods were considered: the common least squares and the fitting of the multinomial distribution representing the number of deuterium atoms exchanged in the individual peptides. Both methods were applied either directly to the isotopic envelope data or to the deuterium distributions obtained by envelope deconvolution (i.e. de-isotoping).Results: An autonomous Matlab script was prepared, based on the exact expressions for the gradient and Hessian of the objective function, with the trust-region algorithm implemented in the compact analytical form recently made available. The least-squares fitting to the envelope data produced the best results, with the greatest precision and good coverage of exact values by the confidence intervals. The deuterium distributions were sensitive to the (simulated) experimental error whose progression by envelope deconvolution caused degradation in accuracy. The multinomial distribution fitting exhibited poor performance due to inadequate representation of the experimental error and missing of the appropriate weight parameters. Some specific peptide arrangement details were discussed as potential sources of ambiguity in the fitting results. Conclusions:The method of fitting to peptide isotopic envelopes has been improved by using the exact gradient and Hessian of the objective function. The fitting should be repeated with different initial guesses in order to find not only the global minimum, but also the local minima with similar depths which may exist due to eventual ambiguity of the fitting results.

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“…Unlike most machine learning methods such as logistic regression, support vector machine, random forest, k-nearest neighbour and arti cial neural network, that uses presence-absence instances dataset for training, the MaxEnt uses presence-background instances dataset for training. MaxEnt is based on the principle, that the probability distribution that maximizes entropy for the current state of knowledge subject to the constraints of the features is the best t model for the phenomenon under consideration (De Martino and De Martino 2018). It is popular primarily because it considers 'minimum assumption' while selecting a probability distribution (Warton 2013).…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

Banerjee¹

2020

Preprint

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The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, population density and proximity to roads are the major determinants of the forest fire. This indicates the role of human activities on the incidences of a forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.77, κ = 0.77). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

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An introduction to the maximum entropy approach and its application to inference problems in biology

Cited by 88 publications

References 129 publications

State-of-the-art in artificial neural network applications: A survey

State-of-the-art in artificial neural network applications: A survey

Resolution of protein hydrogen/deuterium exchange by fitting amide exchange probabilities to the peptide isotopic envelopes

Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

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