BioMedInformatics, Volume 2, Issue 2 (June 2022) – 8 articles

Genomic data enable the development of new biomarkers in diagnostic laboratories. Examples include data from gene expression analyses or metagenomics. Artificial intelligence can help to analyze these data. However, diagnostic laboratories face various technical and regulatory challenges to harness these data. Existing software for genomic data is usually designed for research and does not meet the requirements for use as a diagnostic tool. To address these challenges, we recently proposed a conceptual architecture called “GenDAI”. An initial evaluation of “GenDAI” was conducted in collaboration with a small laboratory in the form of a preliminary study. The results of this pre-study highlight the requirement for and feasibility of the approach. The pre-study also yields detailed technical and regulatory requirements, use cases from laboratory practice, and a prototype called “PlateFlow” for exploring user interface concepts. Full article

Biophotogrammetric methods for postural analysis have shown effectiveness in the clinical practice because they do not expose individuals to radiation. Furthermore, valid statements can be made about postural weaknesses. Usually, such measurements are collected via markers attached to the subject’s body, which can provide conclusions about the current posture. The craniovertebral angle (CVA) is one of the recognized measurements used for the analysis of human head–neck postures. This study presents a novel method to automate the detection of the landmarks that are required to determine the CVA in RGBs. Different image processing methods are applied together with a neuronal network Openpose to find significant landmarks in a photograph. A prominent key body point is the spinous process of the cervical vertebra C7, which is often visible on the skin. Another visual landmark needed for the calculation of the CVA is the ear tragus. The methods proposed for the automated detection of the C7 spinous process and ear tragus are described and evaluated using a custom dataset. The results indicate the reliability of the proposed detection approach, particularly head postures. Full article

Objective: The interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID). Methods: Two challenges in time-series data collected in daily living are addressed: data quality improvement and the detection of unannounced disturbances of BGC. CGM data have missing values for varying periods of time and outliers. People may neglect reporting their meal and physical activity information. In this work, novel methods for preprocessing real-world data collected from people with T1D and the detection of meal and exercise events are presented. Four recurrent neural network (RNN) models are investigated to detect the occurrences of meals and physical activities disjointly or concurrently. Results: RNNs with long short-term memory (LSTM) with 1D convolution layers and bidirectional LSTM with 1D convolution layers have average accuracy scores of 92.32% and 92.29%, and outperform other RNN models. The F1 scores for each individual range from 96.06% to 91.41% for these two RNNs. Conclusions: RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation. Full article

The integration and analysis of multi-omics modalities is an important challenge in bioinformatics and data science in general. A standard approach is to conduct a series of univariate tests to determine the significance for each parameter, but this underestimates the connected nature of biological data and thus increases the number of false-negative errors. To mitigate this issue and to understand how different omics’ data domains are jointly affected, we used the Stacked Regularization model with Bayesian optimization over its full parameter space. We applied this approach to a multi-omics data set consisting of microbiota, metabolites and clinical data from two recent clinical studies aimed at detecting the impact of replacing part of the vegetable fat in infant formula with bovine milk fat on healthy term infants. We demonstrate how our model achieves a high discriminative performance, show the advantages of univariate testing and discuss the detected outcome in its biological context. Full article

Follicular lymphoma (FL) is one of the most frequent subtypes of non-Hodgkin lymphomas. This research predicted the prognosis of 184 untreated follicular lymphoma patients (LLMPP GSE16131 series), using gene expression data and artificial intelligence (AI) neural networks. A new strategy based on the random number generation was used to create 120 different and independent multilayer perceptron (MLP) solutions, and 22,215 gene probes were ranked according to their averaged normalized importance for predicting the overall survival. After dimensionality reduction, the final neural network architecture included (1) newly identified predictor genes related to cell adhesion and migration, cell signaling, and metabolism (EPB41L4B, MOCOS, SPIN2A, BTD, SRGAP3, CTNS, PRB1, L1CAM, and CEP57); (2) the international prognostic index (IPI); and (3) other relevant immuno-oncology, immune microenvironment, and checkpoint markers (CD163, CSF1R, FOXP3, PDCD1, TNFRSF14 (HVEM), and IL10). The performance of this neural network was good, with an area under the curve (AUC) of 0.89. A comparison with other machine learning techniques (C5 tree, logistic regression, Bayesian network, discriminant analysis, KNN algorithms, LSVM, random trees, SVM, tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network) was also made. In conclusion, the overall survival of follicular lymphoma was predicted with a neural network with high accuracy. Full article

Motivation, BRCA1 and BRCA2 are genes with tumor suppressor activity. They are involved in a considerable number of biological processes. To help the biologist in tumor classification, we developed a deep learning algorithm. The question when we want to construct a neural network is how many hidden layers and neurons should we use. If the number of inputs and outputs is defined by the problem, the number of hidden layers and neurons is difficult to define. Hidden layers and neurons that make up each layer of the neural network influence the performance of system predictions. There are different methods for finding the optimal architecture. In this paper, we present the two packages that we have developed, the genetic algorithm (GA) and the particle swarm optimization (PSO) to optimize the parameters of the neural network for predicting BRCA1 and BRCA2 pathogenicity; Results, we will compare the results obtained by the two algorithms. We used datasets collected from our NGS analysis of BRCA1 and BRCA2 genes to train deep learning models. It represents a data collection of 11,875 BRCA1 and BRCA2 variants. Our preliminary results show that the PSO provided the most significant architecture of hidden layers and the number of neurons compared to grid search and GA; Conclusions, the optimal architecture found by the PSO algorithm is composed of 6 hidden layers with 275 hidden nodes with an accuracy of 0.98, precision 0.99, recall 0.98, and a specificity of 0.99. Full article

This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. Geometric features of blood cells are proposed to identify and classify overlapping cells into groups based on different overlapping degrees and the number of overlapped cells. In the proposed method, the percentage of average accuracy for identifying overlapping cells reached 98 percent. The proposed method can segment white blood cells from the overlapping cells with an overlapping degree of 70 percent. Improved Watershed Algorithm successfully increased 36.89 percent of accuracy in WBC segmentation. It reduced 46.12 percent of the over-segmentation problem. Tests of cell counting are conducted in the two stages, which are before and after the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31 percent, the average percentage of WBC counting is 84.8 percent, and the average percentage of RBC counting is 60.55 percent. The proposed method is efficient in identifying and classifying overlapping cells for increasing the accuracy of cell counting. Full article

Metformin is the most used oral anti-diabetic drug in the world and consequently is commonly found in the aquatic environment. Some studies demonstrated that metformin may act as an endocrine-disrupting-chemical (EDC) in fish, although it does not have a classic EDC structure. In this sense, the aim of this work was to evaluate the potential disrupting effect of metformin in the cardiovascular system through in vitro, ex vivo, and in silico studies. For this purpose, human umbilical artery (HUA) and rat aorta artery (RAA) were used. The toxic concentrations of metformin were determined by a cytotoxicity assay and in silico simulations were performed to analyze the interactions of metformin with hormonal receptors. Our results show that metformin decreases viability of the smooth muscle cells. Moreover, metformin induces a vasorelaxant effect in rat aorta and human models by an endothelium-dependent and -independent pathways. Furthermore, docking simulations showed that metformin binds to androgen receptors (AR) and estrogen receptors (ERα and ERβ). In conclusion, the in silico assays suggested that metformin has the potential to be an endocrine disruptor, acting mainly on ERα. Further studies are needed to use metformin in pregnant women without impairing the cardiovascular health of the future generation. Full article