scholarly journals Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting

2018 ◽  
Author(s):  
Etienne Becht ◽  
Yannick Simoni ◽  
Elaine Coustan-Smith ◽  
Maximilien Evrard ◽  
Yang Cheng ◽  
...  
Keyword(s):  
Cell Sorting ◽  
High Performance ◽  
Lymphoid Cells ◽  
High Yield ◽  
Support Vector ◽  
Vector Machines ◽  
A Cell ◽  
Public Datasets ◽  
Yield And Purity ◽  
Current Standards

AbstractMotivationRecent flow and mass cytometers generate 1,000,000 single cell datasets of dimensions 20 to 40. Many tools facilitate the discovery of new cell populations associated with diseases or physiology. These discoveries require the identification of new gating strategies, but gating strategies become exponentially harder to optimize when dimensionality increases. To facilitate this step we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.ResultsHypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of Innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergate’s outputs are consistent with fields’ knowledge and sparser than those from a competing method.Availability and ImplementationHypergate is implemented in R and available at http://github.com/ebecht/hypergate under an Open Source Initiative-compliant licence.

scholarly journals INPUTING STUDENTS’ SCORE BASED ON GIST FEATURES, SUPPORT VECTOR MACHINES AND TESSERACT

2020 ◽  
Vol 1 (41) ◽  
pp. 77-85
Author(s):  
Hau Hung Nguyen
Keyword(s):  
Support Vector Machines ◽  
High Performance ◽  
Regions Of Interest ◽  
Experimental Results ◽  
Support Vector ◽  
Vocational College ◽  
Vector Machines ◽  
Handwritten Digit

Handwriting recogination plays an important role in data inputing and processing in the practice. This attracts much attention of many researchers in different fields. In this paper, a new algorithm is proposed by basing on GIST features, Support Vector Machines (SVM) and Tesseract for entering the score on students’ transcript form at Soc Trang Vocational College. The algorithm consists of two main works, i.e., recognizing students’code and recogziing handwritten digit. In the proposed algorithm, all regions of interest are determined and extract their dictint features with using tesseract and GIST. Then, these features are classified by SVM mechanism. Experimental results demonstrated that the proposed algorithm obtained high performance with accuracy up to 96,57% for students’ code and 93,55% for Handwritting scores. Average time was 7,9s per one transcript.

POLYNOMIAL NETWORKS VERSUS OTHER TECHNIQUES IN TEXT CATEGORIZATION

2008 ◽  
Vol 22 (02) ◽  
pp. 295-322 ◽  
Author(s):  
MAYY M. AL-TAHRAWI ◽  
RAED ABU ZITAR
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
New Techniques ◽  
Vector Machines ◽  
Benchmark Datasets ◽  
Automatic Text

Many techniques and algorithms for automatic text categorization had been devised and proposed in the literature. However, there is still much space for researchers in this area to improve existing algorithms or come up with new techniques for text categorization (TC). Polynomial Networks (PNs) were never used before in TC. This can be attributed to the huge datasets used in TC, as well as the technique itself which has high computational demands. In this paper, we investigate and propose using PNs in TC. The proposed PN classifier has achieved a competitive classification performance in our experiments. More importantly, this high performance is achieved in one shot training (noniteratively) and using just 0.25%–0.5% of the corpora features. Experiments are conducted on the two benchmark datasets in TC: Reuters-21578 and the 20 Newsgroups. Five well-known classifiers are experimented on the same data and feature subsets: the state-of-the-art Support Vector Machines (SVM), Logistic Regression (LR), the k-nearest-neighbor (kNN), Naive Bayes (NB), and the Radial Basis Function (RBF) networks.

scholarly journals (E)-(1-(4-Ethoxycarbonylphenyl)-5-(3,4-dimethoxyphenyl)-3-(3,4-dimethoxystyryl)-2-pyrazoline: Synthesis, Characterization, DNA-Interaction, and Evaluation of Activity Against Drug-Resistant Cell Lines

Molbank ◽  
10.3390/m1114 ◽  
2020 ◽  
Vol 2020 (1) ◽  
pp. M1114
Author(s):  
Matiadis ◽  
Mavroidi ◽  
Panagiotopoulou ◽  
Methenitis ◽  
Pelecanou ◽  
...  
Keyword(s):  
High Performance ◽  
Dna Interaction ◽  
Resistant Cell ◽  
Potential Interaction ◽  
High Yield ◽  
Synergistic Activity ◽  
Viscosity Increase ◽  
Dna Thermal Denaturation ◽  
Dna Lengthening ◽  
Yield And Purity

(E)-1-(4-Ethoxycarbonylphenyl)-5-(3,4-dimethoxyphenyl)-3-(3,4-dimethoxystyryl)-2-pyrazoline was synthesized via the cyclization reaction between the monocarbonyl curcuminoid (2E,6E)-2,6-bis(3,4-dimethoxybenzylidene)acetone and ethyl hydrazinobenzoate in high yield and purity (>95% by High-performance liquid chromatography (HPLC)). The compound has been fully characterized by 1H, 13C NMR, FTIR, UV-Vis and HRMS and its activity was evaluated in terms of its potential interaction with DNA as well as its cytotoxicity against resistant and non-resistant tumor cells. Both DNA thermal denaturation and DNA viscosity measurements revealed that a significant intercalation binding takes place upon treatment of the DNA with the synthesized pyrazoline, causing an increase in melting temperature by 3.53 ± 0.11 °C and considerable DNA lengthening and viscosity increase. However, neither re-sensitisation of Doxorubicin (DO X)-resistant breast cancer and multidrug resistance (MDR) reversal nor synergistic activity with DOX by potentially increasing the DOX cell killing ability was observed.

scholarly journals Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

2020 ◽  
Author(s):  
Roberto Sánchez-Reolid ◽  
María T. López ◽  
Antonio Fernández-Caballero
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
High Performance ◽  
Electrodermal Activity ◽  
Support Vector ◽  
Stress Detection ◽  
Scientific Databases ◽  
Vector Machines ◽  
Detection Of Stress

Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

KDA based WKNN-SVM Method for Activity Recognition System from Smartphone Data

2021 ◽  
Vol 9 (4) ◽  
pp. 0-0
Keyword(s):  
Activity Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Support Vector ◽  
Kernel Discriminant Analysis ◽  
Nearest Neighbours ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Physical Activity Recognition ◽  
Public Datasets

This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as Walking, Standing or Sitting. It combines Weighted K-Nearest Neighbours (WKNN) and Support Vector Machines (SVM). The signals generated from the sensors are processed and then reduced using the Kernel Discriminant Analysis (KDA) by selecting the best discriminating components of the data. We performed different tests on four public datasets where the participants performed different activities carrying a smartphone. We demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances, and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

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