Frontiers Genetics paper

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.

RSOS paper

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.

Bayesian network preprint

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.

Enhancer-based causal inference preprint

There is a lot of research showing that genetic information in combination with gene expression data can be used to predict causal interactions between genes, on the basis that genetic variation among individuals causes gene expression variation but not vice versa (this PLOS CompBio article is a contribution to the field from our group and has links to earlier work). Anagha Joshi’s group asked if this principle could be extended to other contexts, and in a joint preprint “Causal gene regulatory network inference using enhancer activity as a causal anchor” an affirmative answer is given: variation of epigenetic activity at enhancer elements across multiple cell types or experimental treatments together with gene expression data also predicts causal interactions. The accompanying statistical methods have been implemented in our Findr software.

Wisdom of the crowd preprint

We have posted a preprint Wisdom of the crowd from unsupervised dimension reduction on arXiv. In this paper we show that one-dimensional unsupervised dimension reduction, such as principal component analysis and Isomap, can be used to derive consensus predictions from the responses of multiple individuals to the same questions, and performs better than existing solutions. This is relevant for crowd wisdom applications in the social and natural sciences, including data fusion, meta-analysis, crowd-sourcing, and committee decision making.