Hitoshi Iba: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

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scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Patterns ◽

10.1016/j.patter.2020.100139 ◽

2020 ◽

Vol 1 (9) ◽

pp. 100139

Author(s):

Daniel Osorio ◽

Yan Zhong ◽

Guanxun Li ◽

Jianhua Z. Huang ◽

James J. Cai

Keyword(s):

Machine Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory ◽

Cell Data

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A clustering-based approach for inferring recurrent neural networks as gene regulatory networks

Neurocomputing ◽

10.1016/j.neucom.2007.07.023 ◽

2008 ◽

Vol 71 (4-6) ◽

pp. 600-610 ◽

Cited By ~ 28

Author(s):

Wei-Po Lee ◽

Kung-Cheng Yang

Keyword(s):

Neural Networks ◽

Gene Regulatory Networks ◽

Recurrent Neural Networks ◽

Regulatory Networks ◽

Gene Regulatory

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Machine learning methods to reverse engineer dynamic gene regulatory networks governing cell state transitions

10.1101/264671 ◽

2018 ◽

Cited By ~ 2

Author(s):

P. Tsakanikas ◽

D. Manatakis ◽

E. S. Manolakos

Keyword(s):

Machine Learning ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Most Probable Number ◽

State Transitions ◽

Gene Expressions ◽

Dynamic Interactions ◽

Probable Number ◽

Cell State ◽

Gene Regulatory

ABSTRACTDeciphering the dynamic gene regulatory mechanisms driving cells to make fate decisions remains elusive. We present a novel unsupervised machine learning methodology that can be used to analyze a dataset of heterogeneous single-cell gene expressions profiles, determine the most probable number of states (major cellular phenotypes) represented and extract the corresponding cell sub-populations. Most importantly, for any transition of interest from a source to a destination state, our methodology can zoom in, identify the cells most specific for studying the dynamics of this transition, order them along a trajectory of biological progression in posterior probabilities space, determine the "key-player" genes governing the transition dynamics, partition the trajectory into consecutive phases (transition "micro-states"), and finally reconstruct causal gene regulatory networks for each phase. Application of the end-to-end methodology provides new insights on key-player genes and their dynamic interactions during the important HSC-to-LMPP cell state transition involved in hematopoiesis. Moreover, it allows us to reconstruct a probabilistic representation of the “epigenetic landscape” of transitions and identify correctly the major ones in the hematopoiesis hierarchy of states.

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