Dealing with dimensionality: the application of machine learning to multi-omics data.

MOTIVATION: Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. RESULTS: Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sodium pumps adapt spike bursting to stimulus statistics.

Pump activity is a homeostatic mechanism that maintains ionic gradients. Here we examined whether the slow reduction in excitability induced by sodium-pump activity that has been seen in many neuronal types is also involved in neuronal coding. We took intracellular recordings from a spike-bursting sensory neuron in the leech Hirudo medicinalis in response to naturalistic tactile stimuli with different statistical distributions. We show that regulation of excitability by sodium pumps is necessary for the neuron to make different responses depending on the statistical context of the stimuli. In particular, sodium-pump activity allowed spike-burst sizes and rates to code not for stimulus values per se, but for their ratio with the standard deviation of the stimulus distribution. Modeling further showed that sodium pumps can be a general mechanism of adaptation to statistics on the time scale of 1 min. These results implicate the ubiquitous pump activity in the adaptation of neural codes to statistics.

Age-independent synaptogenesis by phosphoinositide 3 kinase.

Synapses are specialized communication points between neurons, and their number is a major determinant of cognitive abilities. These dynamic structures undergo developmental- and activity-dependent changes. During brain aging and certain diseases, synapses are gradually lost, causing mental decline. It is, thus, critical to identify the molecular mechanisms controlling synapse number. We show here that the levels of phosphoinositide 3 kinase (PI3K) regulate synapse number in both Drosophila larval motor neurons and adult brain projection neurons. The supernumerary synapses induced by PI3K overexpression are functional and elicit changes in behavior. Remarkably, PI3K activation induces synaptogenesis in aged adult neurons as well. We demonstrate that persistent PI3K activity is necessary for synapse maintenance. We also report that PI3K controls the expression and localization of synaptic markers in human neuroblastoma cells, suggesting that PI3K synaptogenic activity is conserved in humans. Thus, we propose that PI3K stimulation can be applied to prevent or delay synapse loss in normal aging and in neurological disorders.

Variability in noise-driven integrator neurons.

Neural variability in the presence of noise has been studied mainly in resonator neurons, such as Hodgkin-Huxley or FitzHugh-Nagumo models. Here we investigate this variability for integrator neurons, whose excitability is due to a saddle-node bifurcation of the rest state instead of a Hopf bifurcation. Using simple theoretical expressions for the interspike times distributions, we obtain coefficients of variation in good agreement with numerical calculations in realistic neuron models. The main features of this coefficient as a function of noise depend on the refractory period and on the presence of bistability. The bistability is responsible for the existence of two different time scales in the spiking behavior giving an antiresonance effect.