Learning Tree Distributions by Hidden Markov Models

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We will then sketch a novel non-parametric generalization of the bottom-up hidden tree Markov model with its interpretation as a nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho

Learning from Non-Binary Constituency Trees via Tensor Decomposition

Processing sentence constituency trees in binarised form is a common and popular approach in literature. However, constituency trees are non-binary by nature. The binarisation procedure changes deeply the structure, furthering constituents that instead are close. In this work, we introduce a new approach to deal with non-binary constituency trees which leverages tensor-based models. In particular, we show how a powerful composition function based on the canonical tensor decomposition can exploit such a rich structure. A key point of our approach is the weight sharing constraint imposed on the factor matrices, which allows limiting the number of model parameters. Finally, we introduce a Tree-LSTM model which takes advantage of this composition function and we experimentally assess its performance on different NLP tasks.Comment: Accepted at COLING202

Transition Probabilities of Noise-induced Transitions of the Atlantic Ocean Circulation

The Atlantic Meridional Overturning Circulation (AMOC) is considered to be a tipping element of the climate system. As it cannot be excluded that the AMOC is in a multiple regime, transitions can occur due to atmospheric noise between the present-day state and a weaker AMOC state. For the first time, we here determine estimates of the transition probability of noise-induced transitions of the AMOC, within a certain time period, using a methodology from large deviation theory. We find that there are two types of transitions, with a partial or full collapse of the AMOC, having different transition probabilities. For the present-day state, we estimate the transition probability of the partial collapse over the next 100 years to be about 15%, with a high sensitivity of this probability to the surface freshwater noise amplitude

Molecular dynamics recipes for genome research

Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in sorting pathogenic from neutral genomic mutations. Considering their computational requirements, simulations are commonly performed on HPC computing devices, which are generally expensive and hard to administer. However, variables like the software tool used for modeling and simulation or the size of the molecule under investigation might make one hardware type or configuration more advantageous than another or even make the commodity hardware definitely suitable for MD studies. This work aims to shed lights on this aspect

Inverse modeling of time-delayed interactions via the dynamic-entropy formalism

Even though instantaneous interactions are unphysical, a large variety of maximum-entropy statistical-inference methods match the model inferred and the empirically measured equal-time correlation functions. While this constraint holds when the interaction timescale is much faster than that of the interacting units, as, e.g., in starling flocks (where birds see each other via the electromagnetic field), it fails in a number of counter examples, as, e.g., leukocyte coordination (where signalling proteins diffuse among two cells). Here, by relying upon the Akaike Information Criterion, we relax this assumption and develop a dynamical maximum-entropy framework, which copes with delay in signalling. Our method correctly infers the strength of couplings and fields, but also the time required by the couplings to propagate among the units. We demonstrate the validity of our approach providing excellent results on synthetic datasets generated by the Heisemberg-Kuramoto and Vicsek models. As a proof of concept, we also apply the method to experiments on dendritic migration to prove that matching equal-time correlations results in a significant information loss

A Multi-Layered Study on Harmonic Oscillations in Mammalian Genomics and Proteomics

Cellular, organ, and whole animal physiology show temporal variation predominantly featuring 24-h (circadian) periodicity. Time-course mRNA gene expression profiling in mouse liver showed two subsets of genes oscillating at the second (12-h) and third (8-h) harmonic of the prime (24-h) frequency. The aim of our study was to identify specific genomic, proteomic, and functional properties of ultradian and circadian subsets. We found hallmarks of the three oscillating gene subsets, including different (i) functional annotation, (ii) proteomic and electrochemical features, and (iii) transcription factor binding motifs in upstream regions of 8-h and 12-h oscillating genes that seemingly allow the link of the ultradian gene sets to a known circadian network. Our multifaceted bioinformatics analysis of circadian and ultradian genes suggests that the different rhythmicity of gene expression impacts physiological outcomes and may be related to transcriptional, translational and post-translational dynamics, as well as to phylogenetic and evolutionary components

Multifaceted enrichment analysis of RNA-RNA crosstalk reveals cooperating micro-societies in human colorectal cancer

Alterations in the balance of mRNA and microRNA (miRNA) expression profiles contribute to the onset and development of colorectal cancer. The regulatory functions of individual miRNA-gene pairs are widely acknowledged, but group effects are largely unexplored. We performed an integrative analysis of mRNA–miRNA and miRNA–miRNA interactions using high-throughput mRNA and miRNA expression profiles obtained from matched specimens of human colorectal cancer tissue and adjacent non- tumorous mucosa. This investigation resulted in a hypernetwork-based model, whose functional back- bone was fulfilled by tight micro-societies of miR- NAs. These proved to modulate several genes that are known to control a set of significantly enriched cancer-enhancer and cancer-protection biological processes, and that an array of upstream regulatory analyses demonstrated to be dependent on miR-145, a cell cycle and MAPK signalling cascade master regulator. In conclusion, we reveal miRNA-gene clusters and gene families with close functional relationships and highlight the role of miR-145 as potent upstream regulator of a complex RNA–RNA crosstalk, which mechanistically modulates several signalling path- ways and regulatory circuits that when deranged are relevant to the changes occurring in colorectal carcinogenesis