The Integrated Science Lab invites you to join the conversation at a Lunch Pitch. Magnus Neuman wants to connect with researchers with similarity data, while Jian-Feng Mao wants to connect with computer scientists and biologists to reimagine plant long non-coding RNA identification.
To encourage cross pollination of ideas between researchers from different disciplines, IceLab hosts interdisciplinary research lunches with the vision of allowing ideas to meet and mate. During the Lunch Pitch Season, the creative lunches take place at KBC (Glasburen) on a Wednesday.
Register to come to the pitch and reserve your lunch by Monday, 5 May at 10am.
Note! The default lunch option is a vegetarian falafel wrap. You can choose an alternative lunch in a separate form that will be emailed to you once you have registered.
Researchers across the sciences study similarity data to find clusters of features, such as genes with shared functions (from co-expression data) or species with similar behaviors (from co-occurrence data). The relations between features are often represented as networks, but the methods for analyzing these vary across fields and are often ad-hoc. We suggest using recent progress in network community detection as a general framework for this purpose. This approach reliably extracts meaningful structure even from noisy data while requiring fewer samples - particularly valuable for practical applications.
Interested in: Connecting with researchers who work with similarity data and want to explore new approaches to model selection and regularization.
In today's biological data deluge, where genomic sequencing facilities generate terabytes of information daily across thousands of species, computational tools are essential for knowledge extraction.
We generated a new method, PlantLncBoost, which addresses a critical challenge in plant genomics: identifying long non-coding RNAs (lncRNAs) across diverse species despite their poor sequence conservation. By integrating advanced machine-learning algorithms with strategic feature selection, this method achieves unprecedented accuracy and generalizability in lncRNA identification. This work demonstrates the necessity of continuous computational tool refinement to expand our understanding of biodiversity and extract meaningful insights from overwhelming biological data.
Interested in: We are looking for collaboration from both biologists (to pinpoint critical research questions) and computer scientists (to integrate the latest advancements in computing technology).
KBC Glasburen, near the KBC café. Find your way to the venue (mazemap link)
IceLab Lunch Pitches are made possible through funding from KBC for the venue and lunches and from Stress Response Modeling at IceLab for their coordination.
