scholarly journals An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-Li Hua ◽  
Fa-Zhan Zhang ◽  
Abraham Alemayehu Labena ◽  
Chuan Dong ◽  
Yan-Ting Jin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Targets ◽  
Essential Genes ◽  
Machine Learning Algorithms ◽  
Biological Knowledge ◽  
Independent Dataset ◽  
Novel Approach ◽  
Gene Sets ◽  
Tenfold Cross Validation ◽  
Potential Drug Targets

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset fromSynechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

scholarly journals A Computational Approach for Pathway-Based Systemic Drug Influence

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1063
Author(s):  
Shinuk Kim
Keyword(s):  
Gene Networks ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Drug Efficacy ◽  
Rapid Expansion ◽  
Gene Sets ◽  
Development Risks ◽  
Drug Influence ◽  
Potential Drug Targets ◽  
Systemic Drug

Drug repositioning is a well-known method used to reduce the time, cost, and development risks involved in bringing a new drug to the market. The rapid expansion of high-throughput datasets has enabled computational research that can suggest new potential uses for existing drugs. Some computational methods allow the prediction of potential drug targets of a given disease from a systematic network. Despite numerous efforts, the path of many drugs’ efficacy in the human body remains unclear. Therefore, the present study attempted to understand drug efficacy by systematically focusing on functional gene sets. The purpose of this study was to carry out modeling to identify systemic gene networks (called drug paths) in drug-specific pathways. In our results, we found five different paths for five different drugs.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus

2014 ◽  
Vol 50 ◽  
pp. 29-40 ◽  
Author(s):  
Yao Lu ◽  
Jingyuan Deng ◽  
Judith C. Rhodes ◽  
Hui Lu ◽  
Long Jason Lu
Keyword(s):  
Aspergillus Fumigatus ◽  
Drug Targets ◽  
Essential Genes ◽  
Potential Drug ◽  
Potential Drug Targets

scholarly journals Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

2018 ◽  
Author(s):  
Adam Hakim ◽  
Shira Klorfeld ◽  
Tal Sela ◽  
Doron Friedman ◽  
Maytal Shabat-Simon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
Rank Order ◽  
Marketing Research ◽  
Machine Learning Algorithms ◽  
Great Promise ◽  
Neural Signals ◽  
Novel Approach ◽  
First Time ◽  
Better Than

AbstractA basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects’ individual future preferences over the products and the commercials’ population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials’ effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials’ population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects’ preferences and marketing campaigns’ success.

scholarly journals Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Duy Ngoc Nguyen ◽  
Tuoi Thi Phan ◽  
Phuc Do
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Test Results ◽  
High Confidence ◽  
Knowledge Processing ◽  
Learning Methods ◽  
Novel Approach

AbstractSentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.

Soybean seed vigor discrimination by using infrared spectroscopy and machine learning algorithms

2020 ◽  
Vol 12 (35) ◽  
pp. 4303-4309
Author(s):  
Gustavo Larios ◽  
Gustavo Nicolodelli ◽  
Matheus Ribeiro ◽  
Thalita Canassa ◽  
Andre R. Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Fourier Transform Infrared Spectroscopy ◽  
Learning Algorithms ◽  
Soybean Seed ◽  
Seed Vigor ◽  
Machine Learning Algorithms ◽  
Chemometric Methods ◽  
Novel Approach

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented.

scholarly journals Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

2019 ◽  
Vol 33 ◽  
pp. 2727-2735 ◽  
Author(s):  
Andreas Bunte ◽  
Benno Stein ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Quantitative Model ◽  
Machine Learning Algorithms ◽  
Seamless Integration ◽  
Model Based ◽  
Novel Approach ◽  
Qualitative Models ◽  
Easy Integration ◽  
Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

New Computational Tool Based on Machine-learning Algorithms for the Identification of Rhinovirus Infection-Related Genes

2020 ◽  
Vol 22 (10) ◽  
pp. 665-674 ◽  
Author(s):  
Yan Xu ◽  
Yu-Hang Zhang ◽  
JiaRui Li ◽  
Xiao Y. Pan ◽  
Tao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Pathogenic Mechanism ◽  
Computational Tool ◽  
Blood Samples ◽  
Common Cause ◽  
Rhinovirus Infection ◽  
Novel Approach ◽  
Experimental Approaches

Background: Human rhinovirus has different identified serotypes and is the most common cause of cold in humans. To date, many genes have been discovered to be related to rhinovirus infection. However, the pathogenic mechanism of rhinovirus is difficult to elucidate through experimental approaches due to the high cost and consuming time. Method and Results: In this study, we presented a novel approach that relies on machine-learning algorithms and identified two genes OTOF and SOCS1. The expression levels of these genes in the blood samples can be used to accurately distinguish virus-infected and non-infected individuals. Conclusion: Our findings suggest the crucial roles of these two genes in rhinovirus infection and the robustness of the computational tool in dissecting pathogenic mechanisms.

A Review of Recent Advances and Research on Drug Target Identification Methods

2019 ◽  
Vol 20 (3) ◽  
pp. 209-216 ◽  
Author(s):  
Yang Hu ◽  
Tianyi Zhao ◽  
Ningyi Zhang ◽  
Ying Zhang ◽  
Liang Cheng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Drug Targets ◽  
Target Identification ◽  
Machine Learning Algorithms ◽  
Topological Features ◽  
Drug Target Identification ◽  
Incomplete Datasets ◽  
Optimal Set

Background:From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.Methods:We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.Results:Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.Conclusion:The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods

Sign in / Sign up

Export Citation Format

Share Document