Our major new textbook on the computational aspects of proteomics is out now. All major topics are covered, including peptide sequencing and scoring, protein grouping, quantitation, PTMs, targeted proteomics, DIA, data standards, proteogenomics, and popular software frameworks. See the RSC book site for more information, or head over to Google Books to read the introduction.
We are seeking an outstanding individual, with proven experience in devising computational solutions to data-rich bioscience problems, to help us make a step change in understanding the role that retrotransposable elements play in disease. The project encompasses three areas of cutting edge research: bioinformatics, proteomics and epigenetics. For more details, including how to apply, please visit bit.ly/phd2016b.
All nationalities are eligible to apply for this studentship, which is due to begin in October 2016. The deadline for applications is Sunday 15 May.
Queen Mary University of London is seeking applicants for a unique multidisciplinary studentship, straddling two topical research areas: proteomics and machine learning. The project would be well suited to an exceptional graduate from a physics, engineering, computer science or similar background who is interested in learning more about bioanalytical technologies, and proteomic mass spectrometry in particular.
The student will be supervised primarily by machine learning expert Dr Fabrizio Smeraldi in the School of Electronic Engineering and Computer Science, and will work closely with proteomics experts in the Bessant Lab at the School of Biological and Chemical Sciences.
All nationalities are eligible to apply for this studentship, which is due to begin in late summer 2016. The deadline for applications is Monday 4 April.
For more details, including how to apply, please visit bit.ly/phd2016a.
Following on from the release of our all-in-one classyfire R package on CRAN, we are pleased to announce the publication of the associated paper that explains how it works. classyfire provides a simple solution to building and evaluating machine learning solutions based on support vector machine (SVM) ensembles, using a novel heuristic optimisation approach which trains SVMs in a fraction of the usual time.
Our customised version of Galaxy, GIO, with added support for proteomics and PIT (proteomics informed by trancriptomics) workflows recently made it into Molecular and Cellular Proteomics. We’ve subsequently added a bunch of tools for designing targeted proteomics experiments- head over to GIO to try them out and check out the associated workflow and tutorial.
We are seeking an outstanding individual with proven experience in developing computational solutions for data-rich scientific problems, to help us make a step change in understanding the role that proteins play in disease.
For 2015 entry only, QMUL is offering 86 scholarships of £10,000 each for Masters level study. These awards are jointly funded by Queen Mary University of London (QMUL) and the Government and are intended to help students who might otherwise be prevented from taking up a Masters degree due to financial concerns.