scholarly journals Protein Conformational States—A First Principles Bayesian Method

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1242
Author(s):  
David M. Rogers
Keyword(s):  
Learning Community ◽  
First Principles ◽  
Bayesian Method ◽  
Conformational Transition ◽  
Learning Algorithm ◽  
Structural Detail ◽  
Bayes Classifier ◽  
Automated Identification ◽  
Conformational States ◽  
Classification Schemes

Automated identification of protein conformational states from simulation of an ensemble of structures is a hard problem because it requires teaching a computer to recognize shapes. We adapt the naïve Bayes classifier from the machine learning community for use on atom-to-atom pairwise contacts. The result is an unsupervised learning algorithm that samples a ‘distribution’ over potential classification schemes. We apply the classifier to a series of test structures and one real protein, showing that it identifies the conformational transition with >95% accuracy in most cases. A nontrivial feature of our adaptation is a new connection to information entropy that allows us to vary the level of structural detail without spoiling the categorization. This is confirmed by comparing results as the number of atoms and time-samples are varied over 1.5 orders of magnitude. Further, the method’s derivation from Bayesian analysis on the set of inter-atomic contacts makes it easy to understand and extend to more complex cases.

scholarly journals Detection of Masses in Mammograms using Bayesian Method and Machine learning

2018 ◽  
Vol 7 (4.6) ◽  
pp. 108
Author(s):  
Priyadarshini Chatterjee ◽  
Ch. Mamatha ◽  
T. Jagadeeswari ◽  
Katha Chandra Shekhar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Bayesian Method ◽  
Learning Algorithm ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Breast Masses ◽  
Learning Machine ◽  
Vast Area

Every 100th cases in cancer we come across are of breasts cancer cases. It is becoming very common in woman of all ages. Correct detection of these lesions in breast is very important. With less of human intervention, the goal is to do the correct diagnosis. Not all the cases of breast masses are futile. If the cases are not dealt properly, they might create panic amongst people. Human detection without machine intervention is not hundred percent accurate. If machines can be deeply trained, they can do the same work of detection with much more accuracy. Bayesian method has a vast area of application in the field of medical image processing as well as in machine learning. This paper intends to use Bayesian probabilistic in image segmentation as well as in machine learning. Machine learning in image processing means application in pattern recognition. There are various machine learning algorithms that can classify an image at their best. In the proposed system, we will be firstly segment the image using Bayesian method. On the segmented parts of the image, we will be applying machine learning algorithm to diagnose the mass or the growth.  

scholarly journals A Bayesian Classifier Learning Algorithm Based on Optimization Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sanyang Liu ◽  
Mingmin Zhu ◽  
Youlong Yang
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Bayesian Classifier ◽  
Bayes Classifier ◽  
Independence Assumption ◽  
Maximum Value ◽  
Chi Squared ◽  
Classifier Learning

Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes and affects its classification performance. In this paper, we summarize the existing improved algorithms and propose a Bayesian classifier learning algorithm based on optimization model (BC-OM). BC-OM uses the chi-squared statistic to estimate the dependence coefficients among attributes, with which it constructs the objective function as an overall measure of the dependence for a classifier structure. Therefore, a problem of searching for an optimal classifier can be turned into finding the maximum value of the objective function in feasible fields. In addition, we have proved the existence and uniqueness of the numerical solution. BC-OM offers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.

scholarly journals Transductive Bounds for the Multi-Class Majority Vote Classifier

2019 ◽  
Vol 33 ◽  
pp. 3566-3573
Author(s):  
Vasilii Feofanov ◽  
Emilie Devijver ◽  
Massih-Reza Amini
Keyword(s):  
Learning Algorithm ◽  
Majority Vote ◽  
Confusion Matrix ◽  
Data Sets ◽  
Bayes Classifier ◽  
Partially Labeled Data ◽  
Margin Distribution ◽  
Training Examples ◽  
Multi Class Classification ◽  
Self Learning

In this paper, we propose a transductive bound over the risk of the majority vote classifier learned with partially labeled data for the multi-class classification. The bound is obtained by considering the class confusion matrix as an error indicator and it involves the margin distribution of the classifier over each class and a bound over the risk of the associated Gibbs classifier. When this latter bound is tight and, the errors of the majority vote classifier per class are concentrated on a low margin zone; we prove that the bound over the Bayes classifier’ risk is tight. As an application, we extend the self-learning algorithm to the multi-class case. The algorithm iteratively assigns pseudo-labels to a subset of unlabeled training examples that have their associated class margin above a threshold obtained from the proposed transductive bound. Empirical results on different data sets show the effectiveness of our approach compared to the same algorithm where the threshold is fixed manually, to the extension of TSVM to multi-class classification and to a graph-based semi-supervised algorithm.

scholarly journals A Deep Learning algorithm for accurate and fast identification of coral reef fishes in underwater videos

2018 ◽  
Author(s):  
Sebastien Villon ◽  
David Mouillot ◽  
Marc Chaumont ◽  
Emily S Darling ◽  
Gérard Subsol ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fish Species ◽  
Learning Algorithm ◽  
Decision Rules ◽  
Reef Fishes ◽  
Marine Biodiversity ◽  
Correct Identification ◽  
Automated Identification ◽  
Deep Learning Algorithm ◽  
The One

Identifying and counting individual fish on videos is a crucial task to cost-effectively monitor marine biodiversity, but it remains a difficult and time-consuming task. In this paper, we present a method to assist the automated identification of fish species on underwater images, and we compare our algorithm performances to human ability in terms of speed and accuracy. We first tested the performance of a convolutional neural network trained with different photographic databases while accounting for different post-processing decision rules to identify 20 fish species. Finally, we compared the performance in species identification of our best model with human performances on a test database of 1197 pictures representing nine species. The best network was the one trained with 900 000 pictures of whole fish and of their parts and environment (e.g. reef bottom or water). The rate of correct identification of fish was 94.9%, greater than the rate of correct identifications by humans (89.3%). The network was also able to identify fish individuals partially hidden behind corals or behind other fish and was more effective than humans identification on smallest or blurry pictures while humans were better to recognize fish individuals in unusual positions (e.g. twisted body). On average, each identification by our best algorithm using a common hardware took 0.06 seconds. Deep Learning methods can thus perform efficient fish identification on underwater pictures which pave the way to new video-based protocols for monitoring fish biodiversity cheaply and effectively.

scholarly journals Calculation and Analysis of Fractal Descriptors for Protein Amino Acids in Various Conformational States

2018 ◽  
Vol 1 (3) ◽  
pp. e00070
Author(s):  
V.Yu. Grigorev ◽  
L.D. Grigoreva
Keyword(s):  
Amino Acids ◽  
Conformational Transition ◽  
Van Der Waals ◽  
Conformational State ◽  
Conformational States ◽  
Protein Amino Acids ◽  
Α Helix ◽  
Β Sheet ◽  
Fractal Descriptor

A series of 20 proteinogenic amino acids was studied. Four types of fractal descriptors for 2 conformational states are calculated: α-helix and 1-strand β-sheet. Based on the analysis of the results obtained, it is established that when the conformational state of the amino acids (α-helix→β-sheet) changes, significant changes in the fractal descriptor Dtot, in the calculation of which all the atoms of the molecule are used, are not observed. However, the more specific descriptors Dval, Dvdw and Dunb, which reflect the aggregate of valence-coupled, van der Waals contact and unbound atoms, respectively, are more sensitive to the conformational transition. The increase Dval, Dvdw and the decrease Dunb values were established for a series of 7 amino acids.

scholarly journals Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

2019 ◽  
Vol 9 (5) ◽  
pp. 4452 ◽  
Author(s):  
Chaithra V. D
Keyword(s):  
Social Media ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Video Sharing ◽  
Social Media Site

<p align="justify">Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%.</p>

Variational Bayesian Exploration-Based Active Sarsa Algorithm

2019 ◽  
Vol 33 (10) ◽  
pp. 1951005 ◽  
Author(s):  
Qiming Fu ◽  
Zhengxia Yang ◽  
You Lu ◽  
Hongjie Wu ◽  
Fuyuan Hu ◽  
...  
Keyword(s):  
Learning Process ◽  
Bayesian Method ◽  
Value Function ◽  
Information Gain ◽  
Learning Algorithm ◽  
Exploration And Exploitation ◽  
Variational Bayesian ◽  
Q Learning ◽  
Reward Function ◽  
The Value Function

We proposed an improved variational Bayesian exploration-based active Sarsa (VBE-ASAR) algorithm, which tries to balance the exploration and exploitation dilemma, and speeds up the convergence rate. First, in the learning process, variational Bayesian method is adopted to measure the information gain, which is used as an exploration factor to construct an internal reward function for heuristic exploration. In addition, before the learning process, in order to improve the exploration performance, transfer learning is used to initialize the value function, where Bisimulation metric is introduced to measure the distance between two states from the source MDP and the target MDP, respectively. Finally, we apply the proposed algorithm to the cliff walking problem, and compare with the Sarsa algorithm, the Q-Learning algorithm, the VFT-Sarsa algorithm and the Bayesian Sarsa (BS) algorithm. Experimental results show that the VBE-ASAR algorithm has a faster learning rate.

Multi-Instance Learning with MultiObjective Genetic Programming

2011 ◽  
pp. 1372-1379
Author(s):  
Amelia Zafra
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Learning Community ◽  
Text Categorization ◽  
Learning Algorithm ◽  
Programming Algorithm ◽  
Learning Framework ◽  
Wide Range ◽  
Training Examples

The multiple-instance problem is a difficult machine learning problem that appears in cases where knowledge about training examples is incomplete. In this problem, the teacher labels examples that are sets (also called bags) of instances. The teacher does not label whether an individual instance in a bag is positive or negative. The learning algorithm needs to generate a classifier that will correctly classify unseen examples (i.e., bags of instances). This learning framework is receiving growing attention in the machine learning community and since it was introduced by Dietterich, Lathrop, Lozano-Perez (1997), a wide range of tasks have been formulated as multi-instance problems. Among these tasks, we can cite content-based image retrieval (Chen, Bi, & Wang, 2006) and annotation (Qi and Han, 2007), text categorization (Andrews, Tsochantaridis, & Hofmann, 2002), web index page recommendation (Zhou, Jiang, & Li, 2005; Xue, Han, Jiang, & Zhou, 2007) and drug activity prediction (Dietterich et al., 1997; Zhou & Zhang, 2007). In this chapter we introduce MOG3P-MI, a multiobjective grammar guided genetic programming algorithm to handle multi-instance problems. In this algorithm, based on SPEA2, individuals represent classification rules which make it possible to determine if a bag is positive or negative. The quality of each individual is evaluated according to two quality indexes: sensitivity and specificity. Both these measures have been adapted to MIL circumstances. Computational experiments show that the MOG3P-MI is a robust algorithm for classification in different domains where achieves competitive results and obtain classifiers which contain simple rules which add comprehensibility and simplicity in the knowledge discovery process, being suitable method for solving MIL problems (Zafra & Ventura, 2007).

scholarly journals On artifacts in single-molecule force spectroscopy

2015 ◽  
Vol 112 (46) ◽  
pp. 14248-14253 ◽  
Author(s):  
Pilar Cossio ◽  
Gerhard Hummer ◽  
Attila Szabo
Keyword(s):  
Single Molecule ◽  
Mechanical Response ◽  
Conformational Transition ◽  
Force Spectroscopy ◽  
Conformational States ◽  
Rate Dependent ◽  
Dependent Rate ◽  
Analytic Expressions ◽  
Force Clamp ◽  
Soft Polymer

In typical force spectroscopy experiments, a small biomolecule is attached to a soft polymer linker that is pulled with a relatively large bead or cantilever. At constant force, the total extension stochastically changes between two (or more) values, indicating that the biomolecule undergoes transitions between two (or several) conformational states. In this paper, we consider the influence of the dynamics of the linker and mesoscopic pulling device on the force-dependent rate of the conformational transition extracted from the time dependence of the total extension, and the distribution of rupture forces in force-clamp and force-ramp experiments, respectively. For these different experiments, we derive analytic expressions for the observables that account for the mechanical response and dynamics of the pulling device and linker. Possible artifacts arise when the characteristic times of the pulling device and linker become comparable to, or slower than, the lifetimes of the metastable conformational states, and when the highly anharmonic regime of stretched linkers is probed at high forces. We also revisit the problem of relating force-clamp and force-ramp experiments, and identify a linker and loading rate-dependent correction to the rates extracted from the latter. The theory provides a framework for both the design and the quantitative analysis of force spectroscopy experiments by highlighting, and correcting for, factors that complicate their interpretation.

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