Metabolic networks classification and knowledge discovery by information granulation

Myo-Inositol and D-Chiro-Inositol as Modulators of Ovary Steroidogenesis: A Narrative Review

Myo-inositol is a natural polyol, the most abundant among the nine possible structural isomers available in living organisms. Inositol confers some distinctive traits that allow for a striking distinction between prokaryotes and eukaryotes, the basic clusters into which organisms are partitioned. Inositol cooperates in numerous biological functions where the polyol participates or by furnishing the fundamental backbone of several related derived metabolites, mostly obtained through the sequential addition of phosphate groups (inositol phosphates, phosphoinositides, and pyrophosphates). Overall myo-inositol and its phosphate metabolites display an entangled network, which is involved in the core of the biochemical processes governing critical transitions inside cells. Noticeably, experimental data have shown that myo-inositol and its most relevant epimer D-chiro-inositol are both necessary to permit a faithful transduction of insulin and of other molecular factors. This improves the complete breakdown of glucose through the citric acid cycle, especially in glucose-greedy tissues, such as the ovary. In particular, while D-chiro-inositol promotes androgen synthesis in the theca layer and down-regulates aromatase and estrogen expression in granulosa cells, myo-inositol strengthens aromatase and FSH receptor expression. Inositol effects on glucose metabolism and steroid hormone synthesis represent an intriguing area of investigation, as recent results have demonstrated that inositol-related metabolites dramatically modulate the expression of several genes. Conversely, treatments including myo-inositol and its isomers have proven to be effective in the management and symptomatic relief of a number of diseases associated with the endocrine function of the ovary, namely polycystic ovarian syndrome.

Deep learning neural network derivation and testing to distinguish acute poisonings

INTRODUCTION

Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

RESEARCH DESIGN & METHODS

Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied.

RESULTS

There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively).

CONCLUSION

Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.

Role of Inositols and Inositol Phosphates in Energy Metabolism

Recently, inositols, especially myo-inositol and inositol hexakisphosphate, also known as phytic acid or IP6, with their biological activities received much attention for their role in multiple health beneficial effects. Although their roles in cancer treatment and prevention have been extensively reported, interestingly, they may also have distinctive properties in energy metabolism and metabolic disorders. We review inositols and inositol phosphate metabolism in mammalian cells to establish their biological activities and highlight their potential roles in energy metabolism. These molecules are known to decrease insulin resistance, increase insulin sensitivity, and have diverse properties with importance from cell signaling to metabolism. Evidence showed that inositol phosphates might enhance the browning of white adipocytes and directly improve insulin sensitivity through adipocytes. In addition, inositol pyrophosphates containing high-energy phosphate bonds are considered in increasing cellular energetics. Despite all recent advances, many aspects of the bioactivity of inositol phosphates are still not clear, especially their effects on insulin resistance and alteration of metabolism, so more research is needed.

(Hyper)graph Kernels over Simplicial Complexes

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs.

Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system.

A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.

CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships

Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance. The heterogeneous coupling relationships between features and feature values reflect the characteristics of the real-world categorical data which need to be captured in the representations. The paper proposes an enhanced categorical data embedding method, i.e., CDE++, which captures the heterogeneous feature value coupling relationships into the representations. Based on information theory and the hierarchical couplings defined in our previous work CDE (Categorical Data Embedding by learning hierarchical value coupling), CDE++ adopts mutual information and margin entropy to capture feature couplings and designs a hybrid clustering strategy to capture multiple types of feature value clusters. Moreover, Autoencoder is used to learn non-linear couplings between features and value clusters. The categorical data embeddings generated by CDE++ are low-dimensional numerical vectors which are directly applied to clustering and classification and achieve the best performance comparing with other categorical representation learning methods. Parameter sensitivity and scalability tests are also conducted to demonstrate the superiority of CDE++.

On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

This paper is a continuation of works based on a previously developed new granulation method—homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. Approximation parameters are selected dynamically depending on the degree of homogeneity of decision classes. This makes the method fast and simple, which is an undoubted advantage despite the fact that it gives a slightly lower level of approximation to our other techniques. In this particular article, we are presenting its performance in the process of missing values absorption. We test selected strategies on synthetically damaged data from the UCI repository. The added value is to investigate the specific performance of our new granulation technique in absorbing missing values. The effectiveness of their absorption in the granulation process has been confirmed in our experiments.