A Review on Use of Data Science for Visualization and Prediction of the COVID-19 Pandemic and Early Diagnosis of COVID-19 Using Machine Learning Models

2020 ◽  
pp. 241-265
Author(s):  
Shiv Kumar Choubey ◽  
Harshit Naman
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
Data Science ◽  
Learning Models ◽  
Use Of Data ◽  
Machine Learning Models

scholarly journals Glycowork: A Python package for glycan data science and machine learning

2021 ◽  
Author(s):  
Luc Thomès ◽  
Rebekka Burkholz ◽  
Daniel Bojar
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Data Science ◽  
Biological Processes ◽  
Biological Sequence ◽  
Learning Models ◽  
Related Data ◽  
Strong Focus ◽  
Python Package ◽  
Machine Learning Models

AbstractAs a biological sequence, glycans occur in every domain of life and comprise monosaccharides that are chained together to form oligo- or polysaccharides. While glycans are crucial for most biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include information from these diverse and nonlinear sequences into standard workflows. Here, we present glycowork, an open-source Python package that was designed for the processing and analysis of glycan data by end users, with a strong focus on glycan-related data science and machine learning. Glycowork includes numerous functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models, and learned glycan representations. We envision that glycowork can extract further insights from any glycan dataset and demonstrate this with several workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

scholarly journals Causality in digital medicine

2021 ◽  
Vol 12 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Digital Health ◽  
Imperial College ◽  
Learning Models ◽  
Digital Medicine ◽  
Health Expert ◽  
Causality Inference ◽  
University College London ◽  
Machine Learning Models

AbstractBen Glocker (an expert in machine learning for medical imaging, Imperial College London), Mirco Musolesi (a data science and digital health expert, University College London), Jonathan Richens (an expert in diagnostic machine learning models, Babylon Health) and Caroline Uhler (a computational biology expert, MIT) talked to Nature Communications about their research interests in causality inference and how this can provide a robust framework for digital medicine studies and their implementation, across different fields of application.

scholarly journals Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3045
Author(s):  
Mudabbir Ali ◽  
Asad Masood Khattak ◽  
Zain Ali ◽  
Bashir Hayat ◽  
Muhammad Idrees ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surrogate Model ◽  
Data Science ◽  
End Users ◽  
Learning Models ◽  
Daily Life Activities ◽  
Unseen Data ◽  
Complex Models ◽  
Interpretable Models ◽  
Machine Learning Models

Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine.

scholarly journals Probabilistic Machine Learning for Healthcare

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Y. Chen ◽  
Shalmali Joshi ◽  
Marzyeh Ghassemi ◽  
Rajesh Ranganath
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Model Building ◽  
Generative Models ◽  
Annual Review ◽  
Publication Date ◽  
Biomedical Data ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Dsadvisor: A Tool to Support Predictive Tasks in Data Science

2021 ◽  
Author(s):  
José Augusto Câmara Filho ◽  
José Maria Monteiro
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
New Products ◽  
Learning Models ◽  
Data Repositories ◽  
Deep Knowledge ◽  
Machine Learning Models

Currently, professionals from the most diverse areas of knowledge need to explore their data repositories in order to extract knowledge and create new products or services. Several tools have been proposed in order to facilitate the tasks involved in the Data Science lifecycle. However, such tools require their users to have specific (and deep) knowledge in different areas of Computing and Statistics, making their use practically unfeasible for non-specialist professionals in data science. In this paper, we propose a tool, which aims to encourage non-expert users to build machine learning models to solve predictive tasks, extracting knowledge from their own data repositories. More specifically, DSAdvisor these professionals in predictive tasks involving regression and classification

scholarly journals Profitable Algorithmic Trading Strategy

2021 ◽  
Vol 9 (12) ◽  
pp. 2424-2433
Author(s):  
Prince Nathan S
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Logistic Regression ◽  
Data Analysis ◽  
Data Science ◽  
Principal Component ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Data Set ◽  
Machine Learning Models

Abstract: Cryptocurrency has drastically increased its growth in recent years and Bitcoin (BTC) is a very popular type of currency among all the other types of cryptocurrencies which is been used in most of the sectors nowadays for trading, transactions, bookings, etc. In this paper, we aim to predict the change in bitcoin prices by using machine learning techniques on data from Investing.com. We interpret the output and accuracy rate using various machine learning models. To see whether to buy or sell the bitcoin we created exploratory data analysis from a year of data set and predict the next 5 days change using machine learning models like logistic Regression, Logistic Regression with PCA (Principal Component Analysis), and Neural network. Keywords: Data Science, Machine Learning, Regression, PCA, Neural Network, Data Analysis

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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