scholarly journals Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies

2019 ◽  
Vol 16 (4) ◽  
pp. 1211-1218 ◽  
Author(s):  
Bingqiang Liu ◽  
Ling Han ◽  
Xiangrong Liu ◽  
Jichang Wu ◽  
Qin Ma
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Computational Prediction ◽  
Motif Finding ◽  
Bacterial Genomes ◽  
Sigma 54

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

scholarly journals Machine Learning Strategies When Transitioning between Biological Assays

2021 ◽  
Author(s):  
Staffan Arvidsson McShane ◽  
Ernst Ahlberg ◽  
Tobias Noeske ◽  
Ola Spjuth
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Biological Assays

Computational prediction of origin of replication in bacterial genomes using correlated entropy measure (CEM)

Biosystems ◽  
2015 ◽  
Vol 128 ◽  
pp. 19-25 ◽  
Author(s):  
Harsh Parikh ◽  
Apoorvi Singh ◽  
Annangarachari Krishnamachari ◽  
Kushal Shah
Keyword(s):  
Computational Prediction ◽  
Entropy Measure ◽  
Origin Of Replication ◽  
Bacterial Genomes

scholarly journals Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

2018 ◽  
Vol 20 (3) ◽  
pp. 302-320 ◽  
Author(s):  
Elisa Cuadrado-Godia ◽  
Pratistha Dwivedi ◽  
Sanjiv Sharma ◽  
Angel Ois Santiago ◽  
Jaume Roquer Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Small Vessel Disease ◽  
Cerebral Small Vessel Disease ◽  
Small Vessel ◽  
Vessel Disease

scholarly journals Computational prediction of inter-species relationships through omics data analysis and machine learning

2018 ◽  
Vol 19 (S14) ◽  
Author(s):  
Diogo Manuel Carvalho Leite ◽  
Xavier Brochet ◽  
Grégory Resch ◽  
Yok-Ai Que ◽  
Aitana Neves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Computational Prediction ◽  
Omics Data ◽  
Species Relationships ◽  
Omics Data Analysis

scholarly journals TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

2020 ◽  
Author(s):  
J. Hertzberg ◽  
S. Mundlos ◽  
M. Vingron ◽  
G. Gallone
Keyword(s):  
Machine Learning ◽  
Functional Annotation ◽  
Copy Number Variants ◽  
Enrichment Analysis ◽  
Computational Prediction ◽  
Nucleotide Polymorphisms ◽  
Automated Classification ◽  
Single Nucleotide ◽  
Pathogenic Cnvs ◽  
Disease Impact

AbstractThe computational prediction of disease-associated genetic variation is of fundamental importance for the genomics, genetics and clinical research communities. Whereas the mechanisms and disease impact underlying coding single nucleotide polymorphisms (SNPs) and small Insertions/Deletions (InDels) have been the focus of intense study, little is known about the corresponding impact of structural variants (SVs), which are challenging to detect, phase and interpret. Few methods have been developed to prioritise larger chromosomal alterations such as Copy Number Variants (CNVs) based on their pathogenicity. We address this issue with TADA, a method to prioritise pathogenic CNVs through manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigorous enrichment analysis. We demonstrate that our machine-learning classifiers for deletions and duplications are able to accurately predict pathogenic CNVs (AUC: 0.8042 and 0.7869, respectively) and produce a well-calibrated pathogenicity score. The combination of enrichment analysis and classifications suggests that prioritisation of pathogenic CNVs based on functional annotation is a promising approach to support clinical diagnostic and to further the understanding of mechanisms that control the disease impact of larger genomic alterations.

Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

2017 ◽  
Vol 7 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Prayag Tiwari ◽  
Brojo Kishore Mishra ◽  
Sachin Kumar ◽  
Vivek Kumar
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Maximum Entropy ◽  
Learning Strategies ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
N Gram ◽  
F Measure ◽  
Blog Posts

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.

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