Congratulations to Ammar for successfully defending his PhD thesis “Machine learning approaches for high-dimensional genome-wide association studies”!
You can find more information about the defense and a copy of the thesis on UiB’s pages.
Ammar will now continue in the group as a postdoctoral fellow on the NeuroConvergence project.
Congrats again Dr Malik!
A warm welcome to our new master students: Bendik (master in machine learning) will do a project with Ramin and Gutama about missing data prediction in causal networks, Ørjan (master in bioinformatics) will do a joint project with Anagha Joshi about spatial transcriptomics, and Daniel (master in bioinformatics) will do a joint project with Susanna Röblitz about comparing ML and classical dynamical systems methods for learning networks from time series data.
Ammar has posted a preprint of his paper “rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes” on arXiv. In this paper, Ammar evaluated random forest regression as a method to identify biologically relevant associations by learning models to predict SNPs from imaging features, using data for 518,484 SNPs and 56 brain imaging traits from the ADNI study.
A postdoc position is available in my group on the NeuroConvergence project in collaboration with Jan Haavik and colleagues at the Neurotargeting group. In the NeuroConvergence project, we will use a range of machine learning and causal inference methods on unique large-scale genetic and functional genomic datasets and chemical libraries to identify novel druggable target proteins for neuropsychiatric disorders. The postdoc will also contribute to the in silico protein structural screening and optimization of candidate targets using modern AI tools (RoseTTAfold, Alphafold2). See the job advertisement for more details and application instructions.
A warm welcome to Gutama Ibrahim who joins the group as a PhD student. Gutama has a master in Applied Physics and Mathematics from the University of Tromsø and he will work on the “Intelligent systems for disease risk prediction” project.
Our paper “Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders” has been published in G3. In this paper we analyse the mathematical structure of a class of statistical models for learning hidden factors influencing gene expression data and show that a new algorithm based on the analytical results is orders of magnitude faster than the standard algorithms for solving this class of models.
Congratulations to Ramin, whose paper “A graph feature auto-encoder for the prediction of unobserved node features on biological networks” has been published in BMC Bioinformatics!
Congrats to Ramin for posting no less than two preprints on arXiv:
Both papers result from a collaboration between Ramin and Dariush Salami at the Ambient Intelligence group at Aalto University, and introduce new graph neural network-based methods for analyzing point cloud data.
Great to see when students independently initiate projects and push them through to completion (exposing the limits of their supervisor’s knowledge in the process 🙂