(2013) Generalisable Subcellular Location Analysis

Most of subcellular location analysis in the past has been pursued using datasets that contain multiple copies of the same representative protein (i.e., a specific protein is selected to serve as a nuclear protein, another to serve as a microtubular protein). Algorithms are then evaluated by measuring their ability to recognise this protein. It has then been inferred that the algorithm is thus capable of recognizing location patterns. Unfortunately, this is a leap of judgement that has not been tested. We tested this using a new dataset with multiple proteins standing in the same location group. The algorithms we were using previously performed significantly worse in this new dataset and we thus employed new methods to achieve better results.

The paper describing this work has been published in Bioinformatics:

Determining the subcellular location of new proteins from microscope images using local features by Luis Pedro Coelho, Joshua D. Kangas, Armaghan Naik, Elvira Osuna-Highley, Estelle Glory-Afshar, Margaret Fuhrman, Ramanuja Simha, Peter B. Berget, Jonathan W. Jarvik, and Robert F. Murphy (2013). Bioinformatics, [DOI] [open access version]

Local Features for Bioimage Analysis

We also show that using SURF (a form of local features which achieves excellent results in subcellular classification and we recommend it for bioimage analysis).

This is a video abstract which includes the main results of the paper:

You can also read several blog posts related to this paper.

Here is the full bibtex citation:

@article{Coelho08072013,
author = {Coelho, Luis Pedro and Kangas, Joshua D. and Naik, Armaghan W.
    and Osuna-Highley, Elvira and Glory-Afshar, Estelle and Fuhrman, Margaret
    and Simha, Ramanuja and Berget, Peter B. and Jarvik, Jonathan W. and
    Murphy, Robert F.},
title = {Determining the subcellular location of new proteins from
    microscope images using local features},

year = {2013},
doi = {10.1093/bioinformatics/btt392},
URL = {http://bioinformatics.oxfordjournals.org/content/early/2013/07/07/bioinformatics.btt392.abstract},
eprint = {http://bioinformatics.oxfordjournals.org/content/early/2013/07/07/bioinformatics.btt392.full.pdf+html},
journal = {Bioinformatics}
}

More about generalizable classification...

(2010) Unsupervised Subcellular Pattern Unmixing

The goal of this project was to extend the supervised unmixing methods to an unsupervised setting. The results were surprisingly good: unsupervised is as good as supervised.

@article{Coelho15062010,
    author = {Coelho, Luis Pedro and Peng, Tao and Murphy, Robert F.},
    title = {Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing},
    volume = {26},
    number = {12},
    pages = {i7-i12},
    year = {2010},
    doi = {10.1093/bioinformatics/btq220},
    URL = {http://bioinformatics.oxfordjournals.org/content/26/12/i7.abstract},
    eprint = {http://bioinformatics.oxfordjournals.org/content/26/12/i7.full.pdf+html},
    journal = {Bioinformatics}
}

More about this unsupervised unmixing...

(2009) Nuclear Segmentation

This was an off-shoot of my work on RandTag. The goal was to figure out the best segmentation algorithm for our data in an unbiased, quantitative, way. We built a data set of hand-segmented images and tried out a few algorithms.

Luis Pedro Coelho, Aabid Shariff and Robert F. Murphy, Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms: [DOI link]

@inproceedings{Coelho2009,
    title = {Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms},
    author = {Coelho, Luis Pedro and Shariff, Aabid and Murphy, Robert F.},
    booktitle = {2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
    doi = {10.1109/ISBI.2009.5193098},
    isbn = {978-1-4244-3931-7},
    keywords = {segmentation},
    pages = {518--521},
    year = {2009},
    publisher = {IEEE},
    url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5193098}
}

All of the data and source code are publicly available.

More info about Nuclear Segmentation project

(2009-2010) SLIF: Structured Literature Image Finder

The SLIF Project

On this project, I worked mainly on the image handling half of the pipeline, but also on integration of the whole system. The work I did on the image pipeline included using active learning techniques for dataset generation, and development of multi-class image classifiers.

Slif is available at http://slif.cbi.cmu.edu.

Publisher
This is an overview of the system focused on the visible aspects.
        @article{Ahmed2010,
            title = "Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature",
            journal = "Web Semantics: Science, Services and Agents on the World Wide Web",
            volume = "In Press, Accepted Manuscript",
            number = "",
            pages = " - ",
            year = "2010",
            note = "",
            issn = "1570-8268",
            doi = "DOI: 10.1016/j.websem.2010.04.002",
            url = "http://www.sciencedirect.com/science/article/B758F-4YT6D7G-2/2/348444def95436f515c644e1a539d643",
            author = "Amr Ahmed and Andrew Arnold and Luis Pedro Coelho and Joshua Kangas and Abdul-Saboor Sheikh and Eric Xing and William Cohen and Robert F. Murphy"
        }
        
A slightly technical overview of the SLIF project with a focus on the image processing part. This is a companion paper to the one above.