Protein IR spectra from machine learning

Machine learning approaches to lung cancer prediction from mass spectra.

We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z...

Intensity-based protein identification by machine learning from a library of tandem mass spectra

Atmospheric CO2 retrieved from ground-based near IR solar spectra

[1] The column-averaged volume mixing ratio (VMR) of CO2 over Kitt Peak, Arizona, has been retrieved from high-resolution solar absorption spectra obtained with the Fourier transform spectrometer on the McMath telescope. Simultaneous column measurements of CO2 at 6300 cm 1 and O2 at 7900 cm 1 were ratioed to minimize systematic errors. These column ratios were then scaled by the mean O2 VMR (0....

Machine Learning for Protein Function

Systematic identification of protein function is a key problem in current biology. Most traditional methods fail to identify functionally equivalent proteins if they lack similar sequences, structural data or extensive manual annotations. In this thesis, I focused on feature engineering and machine learning methods for identifying diverse classes of proteins that share functional relatedness bu...

A Machine Learning Application for Classification of Chemical Spectra

This paper presents a software package that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The package, designed for use in conjunction with lab-based spectroscopic instruments, includes features to encourage its adoption by analytical chemists, such as having an intuitive graphical user interface with a step-by-step ‘wizard’ for building new ML ...