scholarly journals Classifying protein structures into folds by convolutional neural networks, distance maps, and persistent homology

2020 ◽  
Author(s):  
Yechan Hong ◽  
Yongyu Deng ◽  
Haofan Cui ◽  
Jan Segert ◽  
Jianlin Cheng
Keyword(s):  
Machine Learning ◽  
Persistent Homology ◽  
Protein Structures ◽  
Distance Matrix ◽  
Significant Loss ◽  
Machine Learning Techniques ◽  
Tertiary Structures ◽  
Distance Map ◽  
Learning Techniques ◽  
And Function

AbstractThe fold classification of a protein reveals valuable information about its shape and function. It is important to find a mapping between protein structures and their folds. There are numerous machine learning techniques to predict protein folds from 1-dimensional (1D) protein sequences, but there are few machine learning methods to directly class protein 3D (tertiary) structures into predefined folds (e.g. folds defined in the SCOP database). We develop a 2D-convolutional neural network to classify any protein structure into one of 1232 folds. We extract two classes of input features for each protein: residue-residue distance matrix and persistent homology images derived from 3D protein structures. Due to restrictions in computing resources, we sample every other point in the carbon alpha chain to generate a reduced distance map representation. We find that it does not lead to significant loss in accuracy. Using the distance matrix, we achieve an accuracy of 95.2% on the SCOP dataset. With persistence homology images of 100 × 100 resolution, we achieve an accuracy of 56% on SCOPe 2.07 dataset. Combining the two kinds of features further improves classification accuracy. The source code of our method (PRO3DCNN) is available at https://github.com/jianlin-cheng/PRO3DCNN.

scholarly journals Structural changes during glass formation extracted by computational homology with machine learning

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Akihiko Hirata ◽  
Tomohide Wada ◽  
Ippei Obayashi ◽  
Yasuaki Hiraoka
Keyword(s):  
Machine Learning ◽  
Glass Formation ◽  
Structural Changes ◽  
Persistent Homology ◽  
Atomic Clusters ◽  
Machine Learning Techniques ◽  
Static Structure ◽  
Atomic Structures ◽  
Computational Homology ◽  
Learning Techniques

AbstractThe structural origin of the slow dynamics in glass formation remains to be understood owing to the subtle structural differences between the liquid and glass states. Even from simulations, where the positions of all atoms are deterministic, it is difficult to extract significant structural components for glass formation. In this study, we have extracted significant local atomic structures from a large number of metallic glass models with different cooling rates by utilising a computational persistent homology method combined with linear machine learning techniques. A drastic change in the extended range atomic structure consisting of 3–9 prism-type atomic clusters, rather than a change in individual atomic clusters, was found during the glass formation. The present method would be helpful towards understanding the hierarchical features of the unique static structure of the glass states.

scholarly journals Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

2021 ◽  
Vol 6 (4) ◽  
pp. 37-44
Author(s):  
Hiral Raja ◽  
Aarti Gupta ◽  
Rohit Miri
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Digit Recognition ◽  
Learning Techniques ◽  
Effective System ◽  
Handwritten Digit ◽  
Digit String ◽  
And Function

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.

scholarly journals Assessing the Role of Machine Learning in Robotics

2020 ◽  
Vol 6 (5) ◽  
pp. 13-15
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Robot Perception ◽  
Learning Techniques ◽  
Complete Detail ◽  
And Function ◽  
The Brain

Machine learning is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Neural framework offers wide support for machine learning algorithms. It is an interface, library or tool which allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. The neural framework is an exceptionally intricate piece of a person that co-ordinate its activities Moreover, tactile data by transmitting signs to and from various pieces of the body. Neural frameworks are applied to perform object gathering and a grasp orchestrating task. Machine Learning techniques have been applied to many sub problems in robot perception – pattern recognition and self-organisation. Modern robot framework which demands a complete detail of each movement of the robot, which breaks the pick-and-spot issue into about free, computationally conceivable sub-issues as a phase toward a comprehensive endeavour level framework

scholarly journals Precomputed Clustering for Movie Recommendation System in Real Time

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Bo Li ◽  
Yibin Liao ◽  
Zheng Qin
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Distance Matrix ◽  
Machine Learning Techniques ◽  
Clustering Methods ◽  
Learning Techniques ◽  
Movie Recommendation ◽  
The Given

A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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