scholarly journals A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub‐epidermal moisture measurements

Author(s):  
Maayan Lustig ◽  
Dafna Schwartz ◽  
Ruth Bryant ◽  
Amit Gefen
2015 ◽  
Author(s):  
Joshua G Stern ◽  
Eric A Gaucher

Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.


2020 ◽  
Vol 8 (6) ◽  
Author(s):  
Zvi Segal ◽  
Kira Radinsky ◽  
Guy Elad ◽  
Gal Marom ◽  
Moran Beladev ◽  
...  

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4 ◽  
Author(s):  
Rami Alkhatib ◽  
Mohamad O. Diab ◽  
Christophe Corbier ◽  
Mohamed El Badaoui

2015 ◽  
Author(s):  
Joshua G Stern ◽  
Eric A Gaucher

Studying the evolutionary history of life’s molecules - DNA, RNA, and protein - reveals nature-based solutions to real-world problems. We discuss an approach to applied molecular evolution that is well-known within the field but may be unfamiliar to a wider audience. Using a case study at the intersection of molecular evolution and medicine, we introduce the fundamental concepts of orthology and paralogy. We also explain a practical entry point to molecular evolution named STORI: Selectable Taxon Ortholog Retrieval Iteratively. STORI is a machine learning algorithm designed to clear a bottleneck that researchers encounter when studying evolution.


2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  

Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.


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