Our group studies how to improve science by automating and optimizing the tools used by domain scientists. We do this primarily by making input specific parameter value choices which help to reduce false information introduced by using less than ideal (or default) parameter choice. Using a framework called Parameter Advising we are able to, without an increase in wall clock time in most cases, find parameter vectors that are much better then the defaults. This framework has been applied to both protein multiple sequence alignment and reference-based transcript assembly, but is very general and can be applied to domains both within and outside of computational biology.
Beyond the algorithm configuration problem, Dr. DeBlasio also has interests in hashing and sketching, primarily focused on minimizer schemes (also called winnowing schemes). Minimizer schemes are a method to represent long strings by some representative k-mer (k length substring) in order to improve the resource consumption of sequence analysis applications (such as genomic read mapping, or document similarity).
This site is currently under development, for details about Dr. DeBlasio’s previous work see his personal website: dandeblasio.com
On Saturday, 30 April 2022 3 members of the lab presented posters at the annual UTEP COURI (Campus Office for Undergraduate Research Initiatives) Symposium. Approximately 115 undergraduates presented 110 posters from across the university. From those, our own Demetrius Hernandez won best poster presentation in the “Engineering, Computational, and Applied Science” category (covered in UTEP news). Congratulations Demetrius!
The lab’s contributions are listed below in no particular order:
- Hector Richart — Increasing protein multiple sequence alignment parameter advising accuracy by considering secondary structure information
- Luis Cedillo — Facet-NN: Improved accuracy estimation using neural networks
- Demetrius Hernandez — Efficient minimizer schemes using deep networks
I will be giving two talks at the UTEP Interdisciplinary Engagement Event on 30 March 2022. My talk, titled “Who needs a manual? Automating scientific tools to accelerate innovation. Building an Automated Scientist: Parameter Advising for Accelerated Discovery” (slides below) will be conducted twice: once at 1:00pm at Station B and once at 3:00pm at Station A in the Interdisciplinary Research Building (IDRB) Room 2.204. The full schedule of presentations can be found here, with the other speakers including colleagues from around campus and including here within Computer Science.
Handout | Slides
Demetrius will present a poster titled “Efficient Minimizer Schemes using Deep Networks” at the 26th Annual International Conference on Research in Computational Molecular Biology (RECOMB) in San Diego, CA in May.
I will be teaching a special topics in data science course this spring. The information can be found below or at http://specialtopics.deblasiolab.org/s22/. The course numbers are CS 4364 for undergrads and CS 5364 for graduates (the CRNs can be found on the CS department course schedule).
Special Topics in Data Science:
Continue reading “Spring 2022 Course”
Algorithms in Computational Biology
I will be teaching a special topics in data science course this spring. The information can be found below or at http://specialtopics.deblasiolab.org/s21/. The course numbers are CS 4364 for undergrads and CS 5364 for graduates (the CRNs can be found on the CS department course schedule).
Special Topics in Data Science:
Continue reading “Spring 2021 Course”
Algorithms for Computational Biology
Dr. DeBlasio will give a talk in the UTEP Biology Department Seminar on Friday 11 October 2019 at 12:30pm in room 2.168 in the Biological Sciences Research Building. This talk gives an overview of his work geared towards building an automated bioinformatician though the application of a framework called Parameter Advising. Below are links to his slides and relevant publications discussed.
Continue reading “Talk in the Biology Department Seminar”
Dr. DeBlasio will be teaching a course in Fall 2019 called “Algorithmic Foundations of Computational Biology” aimed at taking a survey of computational biology from a computer science point of view. We will be covering both classical results such as sequence alignment and Burroughs-Wheeler Transforms as well as emerging topics such as the use of minHash sketches, minimizers, and read-to-graph alignments at the cutting edge of the field.
The course numbers are CS4390/CS5390, meeting Tuesdays and Thursdays 4:30-5:50 in CCSB1.0204. No biological background will be needed.
Continue reading “Fall 2019 Course Offering”