A Novel Approach in Determination of Biofilm Forming Capacity of Bacteria Using Random Forest Classifier

2019 ◽  
pp. 273-279
Author(s):  
Monia Avdić ◽  
Zerina Mašetić ◽  
Ahmed El Sayed ◽  
Lejla Odobašić ◽  
Mirsada Hukić
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Novel Approach

scholarly journals PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets

2020 ◽  
Author(s):  
Léa Pradier ◽  
Tazzio Tissot ◽  
Anna-Sophie Fiston-Lavier ◽  
Stéphanie Bedhomme
Keyword(s):  
Random Forest ◽  
Antibiotic Resistance Genes ◽  
Random Forest Classifier ◽  
Bacterial Genomes ◽  
Large Sets ◽  
Whole Genomes ◽  
Bacterial Plasmid ◽  
Low Sensitivity ◽  
User Friendly

AbstractPlasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. The detection of plasmids in large sets of genomes is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance genes propagation. Several bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds). Here, we present PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in unassembled genomes. This tool is based on the determination of homologies against a database of plasmid sequences, which allow a random forest classifier to discriminate plasmid contigs. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with an accuracy of 98%. Notably, it can detect 96% of plasmid contigs over 50kb with 3.3% of false positives. PlasForest outperforms other currently available tools on test datasets by being both sensitive and precise. We implemented this tool in a user-friendly pipeline that can identify plasmids in large datasets in a reasonable amount of time.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier

The Use of TMAH to Etch Silicon and Expose Metal Bridging Failures

2000 ◽  
Author(s):  
Mark Morris ◽  
James Mohr ◽  
Esteban Ortiz ◽  
Steven Englebretson
Keyword(s):  
Failure Mechanism ◽  
Schottky Diodes ◽  
Tetramethylammonium Hydroxide ◽  
Novel Approach ◽  
Failure Location ◽  
Metal Etching ◽  
Plastic Mold

Abstract Determination of metal bridging failures on plastic encapsulated devices is difficult due to the metal etching effects that occur while removing many of the plastic mold compounds. Typically, the acids used to remove the encapsulation are corrosive to the metals that are found within the device. Thus, decapsulation can result in removal of the failure mechanism. Mechanical techniques are often not successful due to damage that results in destruction of the die and failure mechanism. This paper discusses a novel approach to these types of failures using a silicon etch and a backside evaluation. The desirable characteristics of the technique would be to remove the silicon and leave typical device metals unaffected. It would also be preferable that the device passivation and oxides not be etched so that the failure location is not disturbed. The use of Tetramethylammonium Hydroxide (TMAH), was found to fit these prerequisites. The technique was tested on clip attached Schottky diodes that exhibited resistive shorting. The use of the TMAH technique was successful at exposing thin solder bridges that extruded over the edge of the die resulting in failure.

Methodology for Malware Classification using a Random Forest Classifier

2018 ◽  
Author(s):  
Carlos Domenick Morales-Molina ◽  
Diego Santamaria-Guerrero ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana ◽  
Aldo Hernandez-Suarez
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Malware Classification

scholarly journals Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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