lab.michoel.info
Lingfei’s paper, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering” has been published in Frontiers in Genetics, in a Special Topic on Machine Learning and Network-Driven Integrative Genomics.
In this paper, we present a highly efficient approach for reconstructing Bayesian gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data. The method is implemented in the Findr software.
Pau’s paper “Model-based clustering of multi-tissue gene expression data” has been published in Bioinformatics. In this paper a method, called “revamp”, is introduced to find clusters (groups of genes with shared activity patterns) in multi-tissue data, where gene expression profiles are available from multiple tissues or organs sampled from the same group of individuals. Revamp improves existing methods by its ability to incorporate prior information on physiological tissue similarity, and by identifying a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Revamp is implemented in the Lemon-Tree software.
A belated but nevertheless very warm welcome to Adriaan and Wouter who joined the group in October!
Adriaan is a postdoc with master and PhD degrees in physics who most recently worked as a postdoctoral researcher in the neurophysics group of Jordi Soriani in Barcelona. His focus there was on network inference methods for neuronal activity data, and he will use that experience to further develop our causal gene network inference methods for applications in systems genetics.
Wouter is a master student in bioinformatics and systems biology in Amsterdam who is joining the group for a 6-month internship. He will work on a method for inferring data-driven gene ontologies from gene expression data.
Welcome both!
Lingfei’s paper “Accurate wisdom of the crowd from unsupervised dimension reduction” has been published in Royal Society Open Science. In this paper it is shown that wisdom of the crowd, the collective intelligence derived from responses of multiple individuals to the same questions, is analogous to one-dimensional unsupervised dimension reduction in machine learning. This means that many of-the-shelf dimension reduction methods, such as good old PCA, can be repurposed as crowd-wisdom methods, usually with (much) better performance than existing default crowd-wisdom methods. Perhaps one of the more surprising results concerned the classification of skin images as being cancerous or not. As part of the hype surrounding deep learning, it was recently found that a deep neural network trained on 130,000 images was better at classifying a test set of 111 skin images than 21 individual dermatologists. However, we found that by doing a simple PCA of the predictions of these 21 dermatologists, they collectively outperformed the deep neural network. As The Economist put it in their recent ad, “not all intelligence is artificial”. In fact some of it is collective.
A warm welcome to Ramin Hasibi who has joined the group as a PhD student. Ramin has a master degree in computer networks and a strong background in deep learning and machine learning more generally. Welcome!
Sid’s paper Application of long read sequencing to determine expressed antigen diversity in Trypanosoma brucei infections has been published in PLOS Neglected Tropical Diseases. Congrats again to Sid and everyone who contributed!
I’ll be at the EMBO symposium on regulatory epigenomics next week. Looking forward to it!
A warm welcome to Ammar Malik who has joined the group as a PhD student. Ammar has a degree in Computer Engineering and brings with him a lot of experience in machine learning. Welcome!
A postdoc position is available in my group, to develop machine learning methods for inferring causal gene networks from genome, epigenome and transcriptome sequencing data. For more information and application instructions, see here.
A new preprint, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering”, is available on bioRxiv. Bayesian networks are statistical models for gene regulatory networks, and their inference from large-scale omics data is a major problem in systems genetics. In this paper we present an algorithm to solve this problem that uses causal inference, topological sorting and variable selection, and that is much more efficient than traditional Markov chain Monte Carlo algorithms. The algorithm is implemented in the Findr and lassopv software packages.