scholarly journals High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans

2011 ◽  
Vol 27 (10) ◽  
pp. 1384-1389 ◽  
Author(s):  
Erdal Cosgun ◽  
Nita A. Limdi ◽  
Christine W. Duarte
Keyword(s):  
Machine Learning ◽  
African Americans ◽  
Warfarin Dose ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Continuous Trait ◽  
Dose Prediction ◽  
Learning Techniques

scholarly journals Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5116
Author(s):  
Antonio-Javier Garcia-Sanchez ◽  
Enrique Garcia Angosto ◽  
Jose Luis Llor ◽  
Alfredo Serna Berna ◽  
David Ramos
Keyword(s):  
Machine Learning ◽  
Dose Level ◽  
Machine Learning Techniques ◽  
Axial Tomography ◽  
Analytical Technique ◽  
Dose Prediction ◽  
Regression Curves ◽  
Learning Techniques ◽  
Cell Mutation ◽  
A Minor

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.

Application of Machine Leaning in Drug Discovery and Development

2011 ◽  
pp. 235-256 ◽  
Author(s):  
Shuxing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Model Building ◽  
Pharmaceutical Research ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Lead Discovery ◽  
Drug Discovery And Development ◽  
Learning Techniques ◽  
Machine Leaning

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

scholarly journals Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

PLoS ONE ◽  
2016 ◽  
Vol 11 (5) ◽  
pp. e0155705 ◽  
Author(s):  
Paul Thottakkara ◽  
Tezcan Ozrazgat-Baslanti ◽  
Bradley B. Hupf ◽  
Parisa Rashidi ◽  
Panos Pardalos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Postoperative Complications ◽  
Clinical Data ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Techniques

Learning Binding Affinity from Augmented High Throughput Screening Data

2013 ◽  
pp. 364-385
Author(s):  
Nicos Angelopoulos ◽  
Andreas Hadjiprocopis ◽  
Malcolm D. Walkinshaw
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Techniques

In high throughput screening a large number of molecules are tested against a single target protein to determine binding affinity of each molecule to the target. The objective of such tests within the pharmaceutical industry is to identify potential drug-like lead molecules. Current technology allows for thousands of molecules to be tested inexpensively. The analysis of linking such biological data with molecular properties is thus becoming a major goal in both academic and pharmaceutical research. This chapter details how screening data can be augmented with high-dimensional descriptor data and how machine learning techniques can be utilised to build predictive models. The pyruvate kinase protein is used as a model target throughout the chapter. Binding affinity data from a public repository provide binding information on a large set of screened molecules. The authors consider three machine learning paradigms: Bayesian model averaging, Neural Networks, and Support Vector Machines. The authors apply algorithms from the three paradigms to three subsets of the data and comment on the relative merits of each. They also used the learnt models to classify the molecules in a large in-house molecular database that holds commercially available chemical structures from a large number of suppliers. They discuss the degree of agreement in compounds selected and ranked for three algorithms. Details of the technical challenges in such large scale classification and the ability of each paradigm to cope with these are put forward. The application of machine learning techniques to binding data augmented by high-dimensional can provide a powerful tool in compound testing. The emphasis of this work is on making very few assumptions or technical choices with regard to the machine learning techniques. This is to facilitate application of such techniques by non-experts.

Application of Machine Leaning in Drug Discovery and Development

2012 ◽  
pp. 1460-1482
Author(s):  
Shuxing Zhang
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Model Building ◽  
Pharmaceutical Research ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Lead Discovery ◽  
Drug Discovery And Development ◽  
Learning Techniques ◽  
Machine Leaning

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

scholarly journals A Review on Machine Learning Techniques to Predict Diseases

2019 ◽  
pp. 525-529
Author(s):  
Rohit A Nitnaware ◽  
Prof. Vijaya Kamble
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
The Past ◽  
Course Of Action ◽  
Learning Techniques ◽  
Past Data ◽  
Game Plan

In Disease Diagnosis, affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field, which is used for building the models that can predict the yield relies upon the wellsprings of data, which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm, a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

Learning Binding Affinity from Augmented High Throughput Screening Data

2011 ◽  
pp. 212-234
Author(s):  
Nicos Angelopoulos ◽  
Andreas Hadjiprocopis ◽  
Malcolm D. Walkinshaw
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Techniques

In high throughput screening a large number of molecules are tested against a single target protein to determine binding affinity of each molecule to the target. The objective of such tests within the pharmaceutical industry is to identify potential drug-like lead molecules. Current technology allows for thousands of molecules to be tested inexpensively. The analysis of linking such biological data with molecular properties is thus becoming a major goal in both academic and pharmaceutical research. This chapter details how screening data can be augmented with high-dimensional descriptor data and how machine learning techniques can be utilised to build predictive models. The pyruvate kinase protein is used as a model target throughout the chapter. Binding affinity data from a public repository provide binding information on a large set of screened molecules. The authors consider three machine learning paradigms: Bayesian model averaging, Neural Networks, and Support Vector Machines. The authors apply algorithms from the three paradigms to three subsets of the data and comment on the relative merits of each. They also used the learnt models to classify the molecules in a large in-house molecular database that holds commercially available chemical structures from a large number of suppliers. They discuss the degree of agreement in compounds selected and ranked for three algorithms. Details of the technical challenges in such large scale classification and the ability of each paradigm to cope with these are put forward. The application of machine learning techniques to binding data augmented by high-dimensional can provide a powerful tool in compound testing. The emphasis of this work is on making very few assumptions or technical choices with regard to the machine learning techniques. This is to facilitate application of such techniques by non-experts.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document