scholarly journals Mass Spectrometry and its Applications in Life Sciences

2013 ◽  
Vol 66 (7) ◽  
pp. 719 ◽  
Author(s):  
Costel C. Darie
Keyword(s):  
Mass Spectrometry ◽  
Clinical Significance ◽  
Biomedical Research ◽  
Life Sciences ◽  
Research Front ◽  
Potential Use

Deciphering the biological and clinical significance of the proteins is investigated by mass spectrometry in a relatively new field, named proteomics. Mass spectrometry is, however, also used in chemistry for many years. In this Research Front we try to show the potential use of mass spectrometry in chemical, environmental and biomedical research and also to illustrate the applications of mass spectrometry in proteomics.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

MALDI Imaging Mass Spectrometry – A Novel Approach in Biomedical Research of Tissues

2013 ◽  
Vol 10 (2) ◽  
pp. 76-82
Author(s):  
Magdalena Kalinowska-Herok ◽  
Monika Pietrowska ◽  
Anna Walaszczyk ◽  
Piotr Widak
Keyword(s):  
Mass Spectrometry ◽  
Biomedical Research ◽  
Imaging Mass Spectrometry ◽  
Maldi Imaging ◽  
Novel Approach ◽  
Maldi Imaging Mass Spectrometry

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

Boosting – An Unusual Yet Attractive Optimiser

2014 ◽  
Vol 53 (06) ◽  
pp. 417-418 ◽  
Author(s):  
T. Hothorn
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Statistical Modelling ◽  
Discussion Section ◽  
Boosting Algorithms

SummaryThis editorial is part of a For-Discussion- Section of Methods of Information in Medicine about the papers “The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling” [1] and “Ex-tending Statistical Boosting – An Overview of Recent Methodological Developments” [2], written by Andreas Mayr and co authors. It preludes two discussed reviews on developments and applications of boosting in biomedical research. The two review papers, written by Andreas Mayr, Harald Binder, Olaf Gefeller, and Matthias Schmid, give an overview on recently published methods that utilise gradient or likelihood-based boosting for fitting models in the life sciences. The reviews are followed by invited comments [3] by experts in both boosting theory and applications.

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