Summary of my participation at the GLBIO-2021 conference funded by OBF Event Fellowship

The Open Bioinformatics Foundation (OBF) Event Fellowship program supports and encourages diverse participation at events focusing on open source bioinformatics software development and open science practices in the biological research community. Sona Charles, a Scientist (Bioinformatics) at ICAR-Indian Institute of Spices Research, India, was supported to participate in the Great Lakes Bioinformatics (GLBIO) 2021 Conference by this award granted to her in the application round-1 of 2021. Find more information here.

In this pandemic stricken world when all conferences are taking place virtually and don’t require traveling, it has become more accessible for international participants like me. However, the cost for registration, video-conferencing accessories and childcare remain the same. Luckily, I came across the Open Bioinformatics Foundation Event Fellowship, when I was hoping to attend the Great Lakes Bioinformatics (GLBIO) 2021 Conference. I had already submitted a poster entitled “Sequence-Based Prediction of Phytophthora- Host Interaction Using Machine Learning Methods” to the conference, which was accepted. I went ahead and applied for the event fellowship at Open Bioinformatics Foundation for its first round of 2021. Two weeks later (after the application review phase), I was informed by the Chair of OBF Event Fellowship that I was awarded the fellowship.

Day 1 of GLBIO 2021

The GLBIO conference commenced on 13 May 2021 with the workshops, which is undoubtedly my favourite attraction for attending a conference. Out of all the options for workshops, I chose two: (1) Machine Learning on Microbiome Data: Theory and Practice and Introduction to Deep Learning, and (2) Creating Neural Networks in Python and R. All the code for the workshop as well as the required packages were shared much earlier so that it would be easy for us to follow. As a beginner in Machine Learning, the two workshops enabled me to gain more knowledge on the topics in which I anticipate working in the coming days.

Day 2 of GLBIO 2021

The keynote talk “Variable Selection and Interpretability in ‘Black Box’ Statistical models” took place on 14 May 2021 by Dr Lorin Crawford (screenshot of his title slide below). He introduced the various concepts in Biologically Annotated Neural Networks and their applications in Multiscale genomic inference.

On the same day, I attended four sessions on Knowledge Guided Machine Learning (KGML) in Bio. As a beginner in this area, I really appreciated this session and wanted to learn more about the applications. The session-1 included two talks on predicting the promiscuity of enzymes and Predicting RNA Pseudouridine Sites in Nanopore Sequencing Data. The session-2 included the talks “Knowledge-based Meta-Learning for Cancer Prediction and Survival Analysis” and “Learning to align with differentiable dynamic programming” by Aidong Zhang and Michiel Stock respectively. The session-3 had three informative talks on new machine learning approaches for estimating the functional consequence of mutations in diverse human populations, for diagnostic screening of cardiovascular disease using gut microbiome data and for gut microbiome-based detection of inflammatory bowel diseases. The final session included three talks: (1) Gene signatures of COVID-19 infection severity identified using graph convolutional neural networks on single-cell RNA-Seq data, (2) Machine learning on knowledge graphs and ontologies, and (3) Supervised prediction of ageing-related genes from a weighted dynamic protein-protein interaction network. An EDI panel survey discussion was held between the sessions followed by the poster session. Day 2 of the conference was highly informative and I had a lot of take-home messages and ideas.

Day 3 of GLBIO 2021

Day 3 started with the keynote lecture titled “Methodological advancements to improve metagenomics for surveillance of antimicrobial resistance” by Dr Noelle Noyes (screenshot from her talk).

Metagenomics is one of the areas of interest in my research institute and I attended the Microbiome and Multi-omics analysis sessions. I also attended two sessions on Algorithms and Machine Learning. Another keynote by Michael Osterholm attracted the attention of researchers interested in SARS-Cov2 epidemiology.

It was followed by a session on SARS-CoV2 analysis and a session on proteomics and metadata. The last keynote of GLBIO2021 was on Predicting the evolution of syntenies with closing remarks by Aïda Ouangraoua.

On Day-3, a second poster session was organised where I presented my work titled “Sequence-based prediction of Phytophthora-host interaction using machine learning” (Sona Charles, & Sreekumar J., 2021, DOI: 10.5281/zenodo.4892738). I got to interact with many researchers and receive feedback from a global audience working in the area of machine learning.

Screenshot of the top section of my poster titled: Sequence-based prediction of Phytophthora-host interaction using machine learning, available on Zenodo under CC-BY 4.0 license, http://doi.org/10.5281/zenodo.4892738

Overall the conference was excellent and all data of the conference will be available as an open source resource on YouTube on the ISCB TV channel. I am extremely thankful to the OBF for funding my participation related cost for the conference. I will be able to cover my registration and connection related costs. Since the conference was conducted during lockdown it would have been practically impossible for me to attend the conference if the childcare grant was not awarded for the entire duration of the conference. Since the time zones were overlapping from 21:30 to 03:00 Indian Standard Time (IST), child care was the most essential resource for me for attending the conference throughout the night.

I express my heartfelt thanks for the opportunity to present my research at a reputed international conference with the support of the OBF Event Fellowship. As part of this fellowship, I will soon be depositing my work in my GitHub repository, developed under a CC-BY license (GitHub).         


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