scholarly journals Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

2018 ◽  
Author(s):  
Francisco R. Fields ◽  
Stephan D. Freed ◽  
Katelyn E. Carothers ◽  
Md Nafiz Hamid ◽  
Daniel E. Hammers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Biophysical Parameters ◽  
Isolation And Identification ◽  
Bacterial Membranes ◽  
E Coli ◽  
Amino Acid Peptide ◽  
Design And Testing ◽  
Selection Of ◽  
Minimal Region

AbstractBacteriocins are ribosomally produced antimicrobial peptides that represent an untapped source of promising antibiotic alternatives. However, inherent challenges in isolation and identification of natural bacteriocins in substantial yield have limited their potential use as viable antimicrobial compounds. In this study, we have developed an overall pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for activity. We generated 28,895 total 20-mer peptides and scored them for charge, α-helicity, and hydrophobic moment, allowing us to identify putative peptide sequences with the highest potential for interaction and activity against bacterial membranes. Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination, to create an original approach to rapidly discover novel bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

Identification of power quality events: selection of optimum base wavelet and machine learning algorithm

2019 ◽  
Vol 13 (2) ◽  
pp. 260-271 ◽  
Author(s):  
Faizal Hafiz ◽  
Akshya Swain ◽  
Chirag Naik ◽  
Scott Abecrombie ◽  
Andrew Eaton
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Selection Of

scholarly journals Clinical presentation of COVID-19 – a model derived by a machine learning algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Malik Yousef ◽  
Louise C. Showe ◽  
Izhar Ben Shlomo
Keyword(s):  
Machine Learning ◽  
Clinical Presentation ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Verbal Description ◽  
Machine Learning Algorithm ◽  
Proper Selection ◽  
Selection Of

Abstract COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

How to “Read” a Destination from Images? Machine Learning and Network Methods for DMOs’ Image Projection and Photo Evaluation

2021 ◽  
pp. 004728752199513
Author(s):  
Zeya He ◽  
Ning Deng ◽  
Xiang (Robert) Li ◽  
Huimin Gu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Structural Information ◽  
Semantic Network ◽  
Destination Image ◽  
Content Selection ◽  
Image Projection ◽  
Network Methods ◽  
The Right ◽  
Selection Of

Online photos can reflect tourists’ received destination image and be used to project destination image by destination marketing organizations (DMOs). Studies have identified a gap between projected and received images, highlighting the difficulty DMOs face when selecting content to project the “right” image. Taking an audience-driven perspective, this study analyzed information from user-generated content (UGC) to guide the selection of organization-generated content (OGC) on social media. Using a machine learning algorithm, we extracted connected cognitive and affective elements of received and projected images from UGC and OGC. The elements and their relationships retrieved from UGC were then used to construct a semantic network. The network informs the core–periphery structural information of each element and guides DMOs’ image projection and content selection. Studies with two independent samples demonstrated that an OGC photo whose projected images matched consumers’ central impressions, particularly affective ones, could induce higher online engagement.

Software-Based Testing Kit Using Machine Learning for Diagnosis and Predictive Analytics of COVID-19 Patients

2021 ◽  
Vol 12 (2) ◽  
pp. 39-50
Author(s):  
Vishal Kumar Goar ◽  
Jyoti Prabha
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Prime Factor ◽  
Research Work ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm ◽  
Selection Of

Nowadays, the global community is being affected with COVID-19 disease and integrated infections, which are becoming a menace to the whole world. Research is going on to find out the solution, and still, no particular vaccination or solution has been achieved. This research work is focusing on the analytics of dataset extracted, which has assorted attributes, and these attributes are processed in the machine learning algorithm so that the prime factor can be recognized. In this research manuscript, the usage of COVID-19 dataset is done and trained using supervised learning approach of artificial neural network (ANN) on Levenberg-Marquardt (LM) algorithm so that the predictions of the test patients can be done on the key attributes of age, gender, location, and related parameters. The selection of LM-based implementation with ANN is done as it is the faster approach compared to other functions in neural networks.

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