Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review.

Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.

Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives.

Gap junction (GJ) channels in invertebrates have been used to understand cell-to-cell communication in vertebrates. GJs are a common form of intercellular communication channels which connect the cytoplasm of adjacent cells. Dysregulation and structural alteration of the gap junction-mediated communication have been proven to be associated with a myriad of symptoms and tissue-specific pathologies. Animal models relying on the invertebrate nervous system have exposed a relationship between GJs and the formation of electrical synapses during embryogenesis and adulthood. The modulation of GJs as a therapeutic and clinical tool may eventually provide an alternative for treating tissue formation-related diseases and cell propagation. This review concerns the similarities between Hirudo medicinalis innexins and human connexins from nucleotide and protein sequence level perspectives. It also sets forth evidence of computational techniques applied to the study of proteins, sequences, and molecular dynamics. Furthermore, we propose machine learning techniques as a method that could be used to study protein structure, gap junction inhibition, metabolism, and drug development.

An agent-based model of the fission yeast cell cycle.

The objective of this paper is to develop a computational model of the fission yeast (Schizosaccharomyces pombe) cell cycle using agent-based modeling (ABM), to study the sequence of states of the proteins and time of the cell cycle phases, under the action of proteins that regulate its cell cycle. The model relies only on the conceptual model of the yeast cell cycle regulatory network, where each protein has been represented as an agent with a property called activity that represents its biological function and a stochastic Brownian movement. The results indicate that the simulated phase time did have similar results in comparison with other models using mathematical approaches. Similarly, the correct sequence of states was achieved, and the model was run under different initial states to understand its emergent behaviors. The cell reached the G1 stationary state 94% of the times when running the model under biological initial conditions and 87% of the times when running the model through all the different combinations of initial states. Such results imply that the cell was capable to fix toward the biological expected phenomena. These results show that ABM is a suitable technique to study protein-protein interactions without using, often unavailable, kinetic parameters, or differential equations. This model sets as a base for further studies that involve the cell cycle of the fission yeast, with a special attention to studies and development of drug treatments for specific types of cancer.

Model based on GA and DNN for prediction of mRNA-Smad7 expression regulated by miRNAs in breast cancer.

BACKGROUND: The Smad7 protein is negative regulator of the TGF-ß signaling pathway, which is upregulated in patients with breast cancer. miRNAs regulate proteins expressions by arresting or degrading the mRNAs. The purpose of this work is to identify a miRNAs profile that regulates the expression of the mRNA coding for Smad7 in breast cancer using the data from patients with breast cancer obtained from the Cancer Genome Atlas Project. METHODS: We develop an automatic search method based on genetic algorithms to find a predictive model based on deep neural networks (DNN) which fit the set of biological data and apply the Olden algorithm to identify the relative importance of each miRNAs. RESULTS: A computational model of non-linear regression is shown, based on deep neural networks that predict the regulation given by the miRNA target transcripts mRNA coding for Smad7 protein in patients with breast cancer, with R2 of 0.99 is shown and MSE of 0.00001. In addition, the model is validated with the results in vivo and in vitro experiments reported in the literature. The set of miRNAs hsa-mir-146a, hsa-mir-93, hsa-mir-375, hsa-mir-205, hsa-mir-15a, hsa-mir-21, hsa-mir-20a, hsa-mir-503, hsa-mir-29c, hsa-mir-497, hsa-mir-107, hsa-mir-125a, hsa-mir-200c, hsa-mir-212, hsa-mir-429, hsa-mir-34a, hsa-let-7c, hsa-mir-92b, hsa-mir-33a, hsa-mir-15b, hsa-mir-224, hsa-mir-185 and hsa-mir-10b integrate a profile that critically regulates the expression of the mRNA coding for Smad7 in breast cancer. CONCLUSIONS: We developed a genetic algorithm to select best features as DNN inputs (miRNAs). The genetic algorithm also builds the best DNN architecture by optimizing the parameters. Although the confirmation of the results by laboratory experiments has not occurred, the results allow suggesting that miRNAs profile could be used as biomarkers or targets in targeted therapies.

Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks.

Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were the minimum and maximum values of heart contraction force, the time of ventricular filling, the volume used for dilution, heart rate and weight, volume, length and width of the heart, while the output was the digoxin concentration in dilution necessary to obtain a desired physiological response. ANNs were trained, validated and tested with the dataset of the in vivo experiment results. To select the optimal network, predictions for all the dataset for each configuration of ANNs were made, a maximum 5% relative error for the digoxin concentration was set and the diagnostic accuracy of the predictions made was evaluated. The double-layer perceptron had a barely higher performance than the single-layer perceptron; therefore, both had a good predictive ability. The double-layer perceptron was able to obtain the most accurate predictions of digoxin concentration required in the hearts of T. stultorum using MLP-ANNs.