scholarly journals Machine Learning and Rule Mining Techniques in the Study of Gene Inactivation and RNA Interference

2019 ◽  
Author(s):  
Saurav Mallik ◽  
Ujjwal Maulik ◽  
Namrata Tomar ◽  
Tapas Bhadra ◽  
Anirban Mukhopadhyay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rna Interference ◽  
Gene Inactivation ◽  
Rule Mining

scholarly journals Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining

2019 ◽  
Vol 203 ◽  
pp. 107395 ◽  
Author(s):  
Konstantinos Vougas ◽  
Theodore Sakellaropoulos ◽  
Athanassios Kotsinas ◽  
George-Romanos P. Foukas ◽  
Andreas Ntargaras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Association Rule ◽  
In Silico ◽  
Association Rule Mining ◽  
Drug Response ◽  
Rule Mining ◽  
In Silico Screening ◽  
Screening Process

scholarly journals Efficiency of RNA Interference in the Mouse Hematopoietic System Varies between Cell Types and Developmental Stages

2005 ◽  
Vol 25 (10) ◽  
pp. 3896-3905 ◽  
Author(s):  
Philipp Oberdoerffer ◽  
Chryssa Kanellopoulou ◽  
Vigo Heissmeyer ◽  
Corinna Paeper ◽  
Christine Borowski ◽  
...  
Keyword(s):  
Rna Interference ◽  
Gene Targeting ◽  
Developmental Stages ◽  
Cell Types ◽  
Gene Inactivation ◽  
Loss Of Function ◽  
Homologous Genes ◽  
Family Gene ◽  
Interfering Rna ◽  
Different Cell Types

ABSTRACT RNA interference (RNAi) is a naturally occurring posttranscriptional gene-silencing mechanism that has been adapted as a genetic tool for loss-of-function studies of a variety of organisms. It is more widely applicable than classical gene targeting and allows for the simultaneous inactivation of several homologous genes with a single transgene. Recently, RNAi has been used for conditional and conventional gene inactivation in mice. Unlike gene targeting, RNAi is a dynamic process, and its efficiency may vary both between cell types and throughout development. Here we demonstrate that RNAi can be used to target three separately encoded isoforms of the bcl-2 family gene bfl-1/A1 in a conditional manner in mice. The extent of gene inactivation varies between different cell types and is least efficient in mature lymphocytes. Our data suggest that RNAi is affected by factors beyond small interfering RNA-mRNA stoichiometry.

scholarly journals Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

2022 ◽  
Vol 1 ◽  
Author(s):  
Agostinetto Giulia ◽  
Sandionigi Anna ◽  
Bruno Antonia ◽  
Pescini Dario ◽  
Casiraghi Maurizio
Keyword(s):  
Machine Learning ◽  
16S Rrna ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Microbial Community Composition ◽  
Frequent Itemset ◽  
Supervised Machine Learning ◽  
Rule Mining ◽  
Microbiome Data

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

scholarly journals Machine Learning Based Quantitative Association Rule Mining Method for Evaluating Cellular Network Performance

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166815-166822
Author(s):  
Guanghui Fan ◽  
Wenjuan Shi ◽  
Liang Guo ◽  
Jun Zeng ◽  
Kaixuan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Association Rule ◽  
Association Rule Mining ◽  
Cellular Network ◽  
Network Performance ◽  
Mining Method ◽  
Rule Mining ◽  
Quantitative Association Rule
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