scholarly journals Fiscore Package: Effective Protein Structural Data Visualisation and Exploration

2021 ◽  
Author(s):  
Auste Kanapeckaite
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Mathematical Formulation ◽  
Structural Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Topological Features ◽  
Learning Techniques

Lack of bioinformatics tools to quickly assess protein conformational and topological features motivated to create an integrative and user-friendly R package. Moreover,Fiscore package implements a pipeline for Gaussian mixture modelling making such machine learning techniques readily accessible to non-experts. This is especially important since probabilistic machine learning techniques can help with a better interpretation of complex biological phenomena when it is necessary to elucidate various structural features that might play a role in protein function. Thus,Fiscore package builds on the mathematical formulation of protein physicochemical properties that can aid in drug discovery, target evaluation, or relational database building. Moreover, the package provides interactive environments to explore various features of interest. Finally, one of the goals of this package was to engage structural bioinformaticians and develop more R tools that could help researchers not necessarily specialising in this field. Package Fiscore(v.0.1.2) is distributed via CRAN and Github.

scholarly journals Predictive and interpretable models via the stacked elastic net

2020 ◽  
Author(s):  
Armin Rauschenberger ◽  
Enrico Glaab ◽  
Mark van de Wiel
Keyword(s):  
Machine Learning ◽  
R Package ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Biomedical Sciences ◽  
Molecular Features ◽  
Learning Techniques ◽  
Meta Learning ◽  
Interpretable Models

Abstract Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and Implementation The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

Soft Matter ◽  
2021 ◽  
Author(s):  
Indrajit Tah ◽  
Tristan Sharp ◽  
Andrea Liu ◽  
Daniel Marc Sussman
Keyword(s):  
Machine Learning ◽  
Local Structure ◽  
Biological Tissues ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Glass Forming ◽  
Learning Techniques ◽  
Glass Forming Materials ◽  
The Relationship

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been...

scholarly journals Efficient Machine Learning Techniques to Detect Glaucoma using Structure and Texture based Features

2020 ◽  
Vol 9 (2) ◽  
pp. 193-201
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Texture Features ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
World Health ◽  
Fundus Image ◽  
Glaucoma Detection ◽  
Learning Techniques ◽  
Health Organization

Survey of world health organization has revealed that retinal eye disease Glaucoma is the second leading cause for the blindness worldwide. It is the disease which will steal the vision of the patient without any warning or symptoms. About half of the world Glaucoma patients are estimated to be in Asia. Hence, for social and economic reasons, Glaucoma detection is necessary in preventing blindness and reducing the cost of surgical treatment of the disease. The objective of the paper is to predict and detect Glaucoma efficiently using image processing and machine learning based classification techniques. Segmentation techniques such as unique template approach, Gray Level Coherence Matrix based feature extraction approach and wavelet transform based approach are used to extract these structure and texture based features. Combination of structure based and texture based techniques along with machine learning techniques improves the efficiency of the system. Developed efficient Computer aided Glaucoma detection system classifies a fundus image as either Normal or Glaucomatous image based on the structural features of the fundus image such as Cup-to-Disc Ratio (CDR), Rim-to-Disc Ratio (RDR), Superior and Inferior neuro-retinal rim thicknesses, Vessel structure based features and Distribution of texture features in the fundus images.

scholarly journals Unsupervised seismic facies using Gaussian mixture models

2019 ◽  
Vol 7 (3) ◽  
pp. SE93-SE111 ◽  
Author(s):  
Bradley C. Wallet ◽  
Robert Hardisty
Keyword(s):  
Machine Learning ◽  
Facies Analysis ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Learning Techniques ◽  
Turbidite System ◽  
Seismic Facies Analysis

As the use of seismic attributes becomes more widespread, multivariate seismic analysis has become more commonplace for seismic facies analysis. Unsupervised machine-learning techniques provide methods of automatically finding patterns in data with minimal user interaction. When using unsupervised machine-learning techniques, such as [Formula: see text]-means or Kohonen self-organizing maps (SOMs), the number of clusters can often be ambiguously defined and there is no measure of how confident the algorithm is in the classification of data vectors. The model-based probabilistic formulation of Gaussian mixture models (GMMs) allows for the number and shape of clusters to be determined in a more objective manner using a Bayesian framework that considers a model’s likelihood and complexity. Furthermore, the development of alternative expectation-maximization (EM) algorithms has allowed GMMs to be more tailored to unsupervised seismic facies analysis. The classification EM algorithm classifies data vectors according to their posterior probabilities that provide a measurement of uncertainty and ambiguity (often called a soft classification). The neighborhood EM (NEM) algorithm allows for spatial correlations to be considered to make classification volumes more realistic by enforcing spatial continuity. Corendering the classification with the uncertainty and ambiguity measurements produces an intuitive map of unsupervised seismic facies. We apply a model-based classification approach using GMMs to a turbidite system in Canterbury Basin, New Zealand, to clarify results from an initial SOM and highlight areas of uncertainty and ambiguity. Special focus on a channel feature in the turbidite system using an NEM algorithm shows it to be more realistic by considering spatial correlations within the data.

scholarly journals Enhancement of the Ball Balancing on the Plate using hybrid PD/Machine learning techniques

2021 ◽  
Vol 2128 (1) ◽  
pp. 012028
Author(s):  
Mohamed R. Elshamy ◽  
Essam Nabil ◽  
Amged Sayed ◽  
Belal Abozalam
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mathematical Formulation ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Servo Motor ◽  
Random Forest Regression ◽  
Learning Techniques ◽  
Motor Parameter ◽  
Simulation Results

Abstract This paper discusses an efficient method to improve the balancing and tracking of the trajectory of the BOPS based on machine learning (ML) algorithm with the Pseudo proportional-derivative (PPD) controller. The proposed controller depends on a ML technique that detect the angle of the servo motor required to correct the ball position on the plate. This paper presents three different ML algorithms for the servo motor angle prediction and achieved higher accuracy which are 99.855%, 99.999%, and 99.998% for support vector regression, decision tree regression, and random forest regression, respectively. The simulation results demonstrate that the proposed strategy has significantly improved the settling time and overshoot of the system. The mathematical formulation can be obtained using the Lagrangian formulation and the servo motor parameter obtained by a practical identification experiment.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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