scholarly journals A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis

2021 ◽  
Author(s):  
Marc Raphael ◽  
Michael Robitaille ◽  
Jeff Byers ◽  
Joseph Christodoulides
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Label Cell ◽  
Live Cell ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Cell Segmentation ◽  
Data Set

Abstract Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithm’s initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery’s optical modality, magnification or cell type.

scholarly journals A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis

2021 ◽  
Author(s):  
Michael C. Robitaille ◽  
Jeff M. Byers ◽  
Joseph A. Christodoulides ◽  
Marc P. Raphael
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Label Cell ◽  
Live Cell ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Cell Segmentation ◽  
Data Set

Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithm's initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery's optical modality, magnification or cell type.

scholarly journals Deducing of Optimal Machine Learning Algorithms for Heterogeneity

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Zeyar Aung ◽  
Mohamed Sassi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

scholarly journals Deducing of Optimal Machine Learning Algorithms for Heterogeneity

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Zeyar Aung ◽  
Mohamed Sassi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

A Surveillance on Machine Learning Algorithms and Its Applications

2020 ◽  
Vol 17 (9) ◽  
pp. 4294-4298
Author(s):  
B. R. Sunil Kumar ◽  
B. S. Siddhartha ◽  
S. N. Shwetha ◽  
K. Arpitha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Pros And Cons ◽  
Primary Advantage

This paper intends to use distinct machine learning algorithms and exploring its multi-features. The primary advantage of machine learning is, a machine learning algorithm can predict its work automatically by learning what to do with information. This paper reveals the concept of machine learning and its algorithms which can be used for different applications such as health care, sentiment analysis and many more. Sometimes the programmers will get confused which algorithm to apply for their applications. This paper provides an idea related to the algorithm used on the basis of how accurately it fits. Based on the collected data, one of the algorithms can be selected based upon its pros and cons. By considering the data set, the base model is developed, trained and tested. Then the trained model is ready for prediction and can be deployed on the basis of feasibility.

scholarly journals Exploring Similarities and Differences of Non-European Migrants among Forensic Patients with Schizophrenia

2020 ◽  
Vol 17 (21) ◽  
pp. 7922
Author(s):  
David A. Huber ◽  
Steffen Lau ◽  
Martina Sonnweber ◽  
Moritz P. Günther ◽  
Johannes Kirchebner
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Criminal History ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Psychiatric Clinic ◽  
Schizophrenia Spectrum Disorders ◽  
Related Factors ◽  
Data Set ◽  
Forensic Psychiatric

Migrants diagnosed with schizophrenia are overrepresented in forensic-psychiatric clinics. A comprehensive characterization of this offender subgroup remains to be conducted. The present exploratory study aims at closing this research gap. In a sample of 370 inpatients with schizophrenia spectrum disorders who were detained in a Swiss forensic-psychiatric clinic, 653 different variables were analyzed to identify possible differences between native Europeans and non-European migrants. The exploratory data analysis was conducted by means of supervised machine learning. In order to minimize the multiple testing problem, the detected group differences were cross-validated by applying six different machine learning algorithms on the data set. Subsequently, the variables identified as most influential were used for machine learning algorithm building and evaluation. The combination of two childhood-related factors and three therapy-related factors allowed to differentiate native Europeans and non-European migrants with an accuracy of 74.5% and a predictive power of AUC = 0.75 (area under the curve). The AUC could not be enhanced by any of the investigated criminal history factors or psychiatric history factors. Overall, it was found that the migrant subgroup was quite similar to the rest of offender patients with schizophrenia, which may help to reduce the stigmatization of migrants in forensic-psychiatric clinics. Some of the predictor variables identified may serve as starting points for studies aimed at developing crime prevention approaches in the community setting and risk management strategies tailored to subgroups of offenders with schizophrenia.

scholarly journals Exploring the Efficiency of Various Supervised Machine Learning Techniques to Predict the Heart Disease using Risk Factors

2019 ◽  
Vol 9 (1S) ◽  
pp. 309-312
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Heart Disease ◽  
Major Part ◽  
Data Science ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set

Data Science in healthcare is a innovative and capable for industry implementing the data science applications. Data analytics is recent science in to discover the medical data set to explore and discover the disease. It’s a beginning attempt to identify the disease with the help of large amount of medical dataset. Using this data science methodology, it makes the user to find their disease without the help of health care centres. Healthcare and data science are often linked through finances as the industry attempts to reduce its expenses with the help of large amounts of data. Data science and medicine are rapidly developing, and it is important that they advance together. Health care information is very effective in the society. In a human life day to day heart disease had increased. Based on the heart disease to monitor different factors in human body to analyse and prevent the heart disease. To classify the factors using the machine learning algorithms and to predict the disease is major part. Major part of involves machine level based supervised learning algorithm such as SVM, Naviebayes, Decision Trees and Random forest.

scholarly journals Framework of Intelligent System for Machine Learning Algorithm Selection in Social Sciences

2022 ◽  
pp. 21-28
Author(s):  
Dijana Oreški ◽  
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Algorithm Selection ◽  
Data Set ◽  
Ranking Algorithms ◽  
Meta Learning ◽  
The Right

The ability to generate data has never been as powerful as today when three quintile bytes of data are generated daily. In the field of machine learning, a large number of algorithms have been developed, which can be used for intelligent data analysis and to solve prediction and descriptive problems in different domains. Developed algorithms have different effects on different problems.If one algorithmworks better on one dataset,the same algorithm may work worse on another data set. The reason is that each dataset has different features in terms of local and global characteristics. It is therefore imperative to know intrinsic algorithms behavior on different types of datasets andchoose the right algorithm for the problem solving. To address this problem, this papergives scientific contribution in meta learning field by proposing framework for identifying the specific characteristics of datasets in two domains of social sciences:education and business and develops meta models based on: ranking algorithms, calculating correlation of ranks, developing a multi-criteria model, two-component index and prediction based on machine learning algorithms. Each of the meta models serve as the basis for the development of intelligent system version. Application of such framework should include a comparative analysis of a large number of machine learning algorithms on a large number of datasetsfromsocial sciences.

scholarly journals Amino Acid Composition and Charge Based Prediction of Antisepsis Peptides by Random Forest Machine Learning Algorithm

2021 ◽  
Author(s):  
Aayushi Rathore ◽  
Anu Saini ◽  
Navjot Kaur ◽  
Aparna Singh ◽  
Ojasvi Dutta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Multiple Organ ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Data Set ◽  
Organ Systems

ABSTRACTSepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

scholarly journals Comparing different supervised machine learning algorithms for disease prediction

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Shahadat Uddin ◽  
Arif Khan ◽  
Md Ekramul Hossain ◽  
Mohammad Ali Moni
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Relative Performance ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Different Types

Abstract Background Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a potential application area for these methods. This study aims to identify the key trends among different types of supervised machine learning algorithms, and their performance and usage for disease risk prediction. Methods In this study, extensive research efforts were made to identify those studies that applied more than one supervised machine learning algorithm on single disease prediction. Two databases (i.e., Scopus and PubMed) were searched for different types of search items. Thus, we selected 48 articles in total for the comparison among variants supervised machine learning algorithms for disease prediction. Results We found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies). However, the Random Forest (RF) algorithm showed superior accuracy comparatively. Of the 17 studies where it was applied, RF showed the highest accuracy in 9 of them, i.e., 53%. This was followed by SVM which topped in 41% of the studies it was considered. Conclusion This study provides a wide overview of the relative performance of different variants of supervised machine learning algorithms for disease prediction. This important information of relative performance can be used to aid researchers in the selection of an appropriate supervised machine learning algorithm for their studies.

P6568Forecasting atrial fibrillation using machine learning techniques

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J.-M Gregoire ◽  
N Subramanian ◽  
D Papazian ◽  
H Bersini
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Neural Net ◽  
Data Set ◽  
Rr Intervals ◽  
Learning Techniques

Abstract Background Forecasting atrial fibrillation (AF) a few minutes before its onset has been studied, mainly based on heart rate variability parameters, derived from 24-hour ECG Holter monitorings. However, these studies have shown conflicting, non-clinically applicable results. Nowadays, machine learning algorithms have proven their ability to anticipate events. Therefore, forecasting AF before its onset should be (re)assessed using machine learning techniques. A reliable forecasting could improve results of preventive pacing in patients with cardiac electronic implanted devices (CEID). Purpose To forecast an oncoming AF episode in individual patients using machine learning techniques. To evaluate the effect if the onset of an AF episode can be forecasted on longer time frames. Methods The totality of the raw data of a data set of 10484 ECG Holter monitorings was retrospectively analyzed and all AF episodes were annotated. Onset of each AF episode was determined with a precision of 5 msec. We only took AF events into consideration if they lasted longer than 30 seconds. Of all patients in the dataset, 140 presented paroxysmal AF (286 recorded AF episodes). We only used RR intervals to predict the presence of AF. We developed two different types of machine learning algorithms with different computational power requirements: a “dynamic” deep and recurrent neural net (RNN) and a “static” decision-tree with adaboost (boosting trees) more suitable for embedded devices. These algorithms were trained on one set of patients (around 90%) and tested on the remaining set of patients (around 10%). Results The performance figures are summarized in the table. Both algorithms can be tuned to increase their specificity (at a loss of sensitivity) or vice versa, depending on the objective. Performance of forecasting algorithms RR-distance Boosting trees AUC RNN AUC 30–1 RR-Interval(s) before an AF event 97.1% 98.77% 60–31 RR-Intervals before an AF event 97.5% 99.1% 90–61 RR-Intervals before an AF event 96.9% 99.1% 120–91 RR-Inervals before an AF event 98.2% 98.9% AUC for Area Under ROC Curves. Conclusion Based upon this retrospective study, we show that AF can be forecasted on an individual level with high predictive power using machine learning algorithm, with little drop-off of predictive value within the studied distances (1–120 RR intervals before a potential AF episode). We believe that the embedding of our new algorithm(s) in CEID's could open the way to innovative therapies that significantly decrease AF burden in selected implanted patients.

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