scholarly journals Impact of Physico-Chemical Properties for Soils Type Classification of OAK using different Machine Learning Techniques

2019 ◽  
Vol 177 (17) ◽  
pp. 38-44
Author(s):  
Himanshu Pant ◽  
Manoj C. ◽  
Ashutosh Bhatt
Keyword(s):  
Machine Learning ◽  
Chemical Properties ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Physico Chemical ◽  
Type Classification

scholarly journals Machine learning for the structure–energy–property landscapes of molecular crystals

2018 ◽  
Vol 9 (5) ◽  
pp. 1289-1300 ◽  
Author(s):  
Félix Musil ◽  
Sandip De ◽  
Jack Yang ◽  
Joshua E. Campbell ◽  
Graeme M. Day ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Crystals ◽  
Chemical Properties ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physical Chemical ◽  
Learning Techniques ◽  
Structure Calculations ◽  
Energy Property

Polymorphism is common in molecular crystals, whose energy landscapes usually contain many structures with similar stability, but very different physical–chemical properties. Machine-learning techniques can accelerate the evaluation of energy and properties by side-stepping accurate but demanding electronic-structure calculations, and provide a data-driven classification of the most important molecular packing motifs.

scholarly journals Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hossein Khabbaz ◽  
Mohammad Hossein Karimi-Jafari ◽  
Ali Akbar Saboury ◽  
Bagher BabaAli
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Antimicrobial Peptides ◽  
Mammalian Cells ◽  
Chemical Properties ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Physico Chemical ◽  
Machine Learning Model ◽  
Low Toxicity

Abstract Background Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed. Results In this study, by using an up-to-date dataset, a machine learning model has been trained successfully to predict the toxicity of antimicrobial peptides. The comprehensive set of features of both physico-chemical and linguistic-based with local and global essences have undergone feature selection to identify key properties behind toxicity of antimicrobial peptides. After feature selection, the hybrid model showed the best performance with a recall of 0. 876 and a F1 score of 0. 849. Conclusions The obtained model can be useful in extracting AMPs with low toxicity from AMP libraries in clinical applications. On the other hand, several properties with local nature including positions of strand forming and hydrophobic residues in final selected features show that these properties are critical definer of peptide properties and should be considered in developing models for activity prediction of peptides. The executable code is available at https://git.io/JRZaT.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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