At this year’s Bioinformatics Day organized by the Finnish Society for Bioinformatics held on May 11th at Turku, Dr. Huibin Shen was awarded for the best Finnish Bioinformatics Ph.D. thesis done in 2016-2017. In total, there were 6 high-level nominees and Dr. Huibin Shen was selected because of the high impact of his work in terms of identification of small molecules through MS/MS mass spectrometer data analysis. The thesis is titled “Machine Learning for Small Molecule Identification” and it was accepted at the School of Science, Aalto University in 2017. Below, you can find the review board’s statement:
The Finnish Society for Bioinformatics invited us to review the PhD theses that were nominated for “the best bioinformatics thesis in Finland award for the years 2016-2017” and to select one for the award.
After the review, we conclude that all nominated theses were of high quality and each made significant contributions to advance the bioinformatics and the selection of the best one was tough. After consideration of the overall quality and impact of the work, we unanimously selected Dr. Huibin Shen as the recipient of the best thesis award.
In his thesis, Dr. Shen proposes a pipeline based on machine learning algorithms for the identification of small molecules through MS/MS mass spectrometer data analysis. This is a very difficult and important problem, that have applications in many health science fields. Key methodology that the work is based on is multiple kernel learning methods that are applied elegantly.
Thesis includes six publications and many of these have been published in top journals, including Bioinformatics (2 papers) and PNAS. These papers have collected high number of citations in relatively short time, for example PNAS paper published in 2015 has been cited 116 times according to Google Scholar demonstrating the impact of the work.
We wish to congratulate Dr. Shen on the work well done and are happy to nominate him as the recipient of the best thesis award.