scholarly journals Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143465 ◽  
Author(s):  
Bahareh Torkzaban ◽  
Amir Hossein Kayvanjoo ◽  
Arman Ardalan ◽  
Soraya Mousavi ◽  
Roberto Mariotti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Microsatellite Variation

scholarly journals Classification of Microglial Morphological Phenotypes Using Machine Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Judith Leyh ◽  
Sabine Paeschke ◽  
Bianca Mages ◽  
Dominik Michalski ◽  
Marcin Nowicki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Microglial Activation ◽  
Cell Function ◽  
Morphological Plasticity ◽  
Brain Regions ◽  
Automated Classification ◽  
Time Saving ◽  
Morphological Evaluation

Microglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples.

scholarly journals An adaptable analysis workflow for characterization of platelet spreading and morphology

2020 ◽  
Author(s):  
Jeremy A. Pike ◽  
Victoria A. Simms ◽  
Christopher W. Smith ◽  
Neil V. Morgan ◽  
Abdullah O. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Open Source Software ◽  
Machine Learning Methods ◽  
Large Numbers ◽  
Analysis Workflow ◽  
Platelet Spreading ◽  
Automated Protocol

AbstractThe assessment of platelet spreading through light microscopy, and the subsequent quantification of parameters such as surface area and circularity, is a key assay for many platelet biologists. Here we present an analysis workflow which robustly segments individual platelets to facilitate the analysis of large numbers of cells while minimising user bias. Image segmentation is performed by interactive learning and touching platelets are separated with an efficient semi-automated protocol. We also use machine learning methods to robustly automate the classification of platelets into different subtypes. These adaptable and reproducible workflows are made freely available and are implemented using the open source software KNIME and ilastik.

scholarly journals Characterization of Potential Drug Treatments for COVID-19 using Twitter

2020 ◽  
Author(s):  
Ramya Tekumalla ◽  
Juan M Banda
Keyword(s):  
Machine Learning ◽  
Language Use ◽  
Additional Data ◽  
Antiviral Treatment ◽  
Potential Drug ◽  
Machine Learning Methods ◽  
Global Pandemic ◽  
Drug Treatments

Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommended for COVID-19, there are several drugs that can potentially help with symptoms. In this work, we mined a large twitter dataset of 280 million tweets of COVID-19 chatter to identify discourse around potential treatments. While seemingly a straightforward task, due to the informal nature of language use in Twitter, we demonstrate the need of machine learning methods to aid in this task. By applying these methods we are able to recover almost 21% additional data than with traditional methods.

scholarly journals CLASSIFICATION OF SEISMIC PHASES BASED ON MACHINE LEARNING

2020 ◽  
Vol 5 (333) ◽  
pp. 33-42
Author(s):  
Nurtas Marat ◽  
◽  
Baishemirov Zharasbek ◽  
Tastanov Madi ◽  
Zhanabekov Zhandos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Wave ◽  
Seismic Waves ◽  
Time Window ◽  
Time Windows ◽  
Three Dimensional ◽  
Multi Scale ◽  
Unit Circles

In the course of recent years, progresses in sensor innovation has lead to increments in the interest for automated strategies for investigating seismological signals. Fundamental to the comprehension of the components creating seismic signals is the information on the phases of seismic waves. Having the option to indicate the kind of wave prompts better performing seismic forecasting frameworks. In this article, we propose another strategy for the characterization of seismic waves quantification from a three-channel seismograms. The seismograms are isolated into covering time windows, where each time-window is mapped to a lot of multi-scale three-dimensional unitary vectors that portray the direction of the seismic wave present in the window at a few physical scales. The issue of arranging seismic waves gets one of ordering focuses on a few two-dimensional unit circles. We take care of this issue by utilizing kernel based machine learning that are remarkably adjusted to the geometry of the circle. The grouping of the seismic wave depends on our capacity to gain proficiency with the limits between sets of focuses on the circles related with the various kinds of seismic waves. At each signal scale, we characterize a thought of vulnerability connected to the order that considers the geometry of the dissemination of tests on the circle. At long last, we join the grouping results acquired at each scale into a unique label.

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

2018 ◽  
Vol 72 (12) ◽  
pp. 1774-1780 ◽  
Author(s):  
Irene Marivel Nolasco Perez ◽  
Amanda Teixeira Badaró ◽  
Sylvio Barbon ◽  
Ana Paula AC Barbon ◽  
Marise Aparecida Rodrigues Pollonio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Processing Industry ◽  
Chemical Attributes ◽  
Physical And Chemical

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical–chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900–1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

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.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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