Mapping organizational complexity: a network-based approach to behavior through machine learning tools

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Air quality has an effect on a population’s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is reflected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in different urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artificial neural networks and support vector machines are the most widely used to predict different types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.

Integrating machine learning into the in silico clinical trial pipeline.

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

Tandem mass spectrometry (TMS) has emerged an important screening tool for various metabolic disorders in newborns. However, there is inherent risk of false positive outcomes. Objective To establish analyte-specific cutoffs in TMS by integrating metabolomics and genomics data to avoid false positivity and false negativity and improve its clinical utility. TMS was performed on 572 healthy and 3000 referred newborns. Urine organic acid analysis identified 23 types of inborn errors in 99 referred newborns. Whole exome sequencing was performed in 30 positive cases. The impact of physiological changes such as age, gender, and birthweight on various analytes was explored in healthy newborns. Machine learning tools were used to integrate demographic data with metabolomics and genomics data to establish disease-specific cut-offs; identify primary and secondary markers; build classification and regression trees (CART) for better differential diagnosis; for pathway modeling. This integration helped in differentiating B12 deficiency from methylmalonic acidemia (MMA) and propionic acidemia (Phi coefficient=0.93); differentiating transient tyrosinemia from tyrosinemia type 1 (Phi coefficient=1.00); getting clues about the possible molecular defect in MMA to initiate appropriate intervention (Phi coefficient=1.00); to link pathogenicity scores with metabolomics profile in tyrosinemia (r2=0.92). CART model helped in establishing differential diagnosis of urea cycle disorders (Phi coefficient=1.00). Calibrated cut-offs of different analytes in TMS and machine learning-based establishment of disease-specific thresholds of these markers through integrated OMICS have helped in improved differential diagnosis with significant reduction of the false positivity and false negativity rates.

Introduction: Treatment of upper gastrointestinal bleeding (UGIB) is a complex challenge due to the wide range of causes and factors affecting hospitalization outcomes.Objective: To study the impact of various factors on 30-day hospital outcomes using machine learning (ML) tools.Materials and methods: We compiled a retrospective data set that includes clinical, laboratory, and imaging data of 101 patients. The database was divided into 2 groups by UGIB etiology: ulcer and variceal bleedings. Both etiological groups were processed using ML tools in 2 steps: imputation by the MICE (multiple imputation by chained equations) model and factor importance analysis using the Random Forest model.Results: Analysis revealed that the most prognostically valuable parameters in both groups were well-known mortality predictors and emerging predictive factors, such as creatinine, blood pressure, activated partial thromboplastin time, level of consciousness, urea, lactate, comorbidity status, procalcitonin, ferritin, and total protein.Conclusions: The application of advanced tools confirmed the significance of popular and validated mortality predictors and contributed to the development of predictors, both explored and unexplored ones.

Software-Defined Networking (SDN) enhances network management by separating the control and data planes into controllers and switches, allowing for centralized, programmable networks with multiple controllers. Switches are mapped to controllers and exchange control messages to manage the network, which leads to significant energy consumption. Managing energy in networks has become a critical issue, as dynamic changes in switch loads can cause controller overloads, necessitating the migration of switches to other controllers. As networks grow, energy consumed in control communications becomes a major concern. This paper proposes an unsupervised learning Artificial Neural Network (ANN) model to address controller overloads and optimize energy consumption, achieving faster execution times compared to conventional methods while maintaining manageable energy efficiency. The model considers dynamic switch loads and the hop distance between switches and controllers when remapping switches to optimize energy use. Experimental results demonstrate that the proposed unsupervised ANN model performs effectively in large networks, enabling efficient handling of controller overloads during variations in switch loads. The adaptability of the ANN model provides a robust strategy for energy-efficient load distribution, enhancing the scalability and efficiency of SDN environments.

Background: A Liver tumor is a dangerous disease that may leads to death, the chances of survival will increase when it is detected in early time. Materials and Methods: In this study, clustering Fuzzy c-mean and ''Self-Organization Feature Map'', HSOFM, which is an unsupervised artificial neural network, based on the histogram of the image, are presented for segmenting, isolating, and then extracting tumors and other abnormal regions in liver images of MRI and CT imaging. In an additional processing, morphological operations were used to achieve the complete final extraction of the isolated regions without any extra pixels that do not belong to the abnormal regions. These two methods were applied on five MRI and four CT scan images. The extracted regions surface areas were calculated and compared with the groundtruth, manually extracted mass regions, to check the goodness adequacy of the adopted methods. The work was achieved by Mat Lab programing environment. Results: The percent relative differences of the extracted abnormal regions by implementing the adopted methods with the groundtruth were ranged from 1.108 % to 3.861 % for the images of MRI, while for CT scan images, the percent relative differences were in the range from 0.732 % to 3.456%. By implementing FCM clustering, the percent relative difference ranged from 0.724% to 4.370 % for three MRI images, while for CT scan images, the percent relative difference was 4.327 % for one of the adopted CT images. Results of implementing HSOFM indicate the high-quality performance of this method with 95 % accuracy. Conclusion: From the results we can conclude that there is an appropriate number of nodes and clusters that is a more adequate choice than others depending on the properties’ intensity variance in each processed image. The comparison between the two implemented methods figures out the superiority of the HSOFM method over FCM. There are some limitations, such as the small size of the abnormal regions as well as the interference of the abnormal regions with the normal regions of the similar intensity that require applying advanced enhancement methods as an additional preprocessing step.

Most team sports are characterized by positions to which the players in a team are assigned. The goal of this classification is to attribute specific responsibilities during a game. Moreover, the same classification drives the buying and selling of players according to team managers and coaches strategies. The existing positions - often defined a long time ago - tend to reflect traditional points of view about the game and sometimes they are no longer well-suited to the new concepts arisen with the evolution of the way of playing. This paper focuses on basketball and aims at describing new roles of players during the game, by means of the analysis of players' performance statistics with data mining and machine learning tools. In detail, self-organizing maps and fuzzy clustering procedures are adopted in tandem to define groups of players with similar way of playing. The results show that, when considering the modern basketball players' statistics, classical positions are not able to fully represent their way of playing, and a new set of 5 roles emerges as a meaningful classification of players' characteristics.

Allele Specific Expression data quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites. Current methodologies of genome-wide sequencing produce large amounts of missing data that may affect statistical inference and bias the outcome of experiments. Machine learning tools could be employed to explore the data and to estimate missing signatures. We present a two-phase procedure based on Self-Organizing Maps (SOMs), an unsupervised clustering technique, to recover missing allele specific expression data from RNA-seq experiments. Specifically, a SOM trained on a complete population is used to assign a so-called corrupted individual to its most fitting cluster ; then, a completion rule based on allele frequencies within the subpopulation of defined by is employed to reconstruct . To evaluate our approach, we first apply it to purely artificial datasets, in order to have full control over all experimental conditions. After that, we consider a real population of Vitis vinifera, which we also extend by applying a computational framework to generate synthetic individuals from allele expression data. We then introduce two local feature relevance indices in order to assess the influence of specific alleles on the topological placement of corrupted individuals in the SOM structure. Our results, showing promising accuracy in the prediction of missing alleles, suggest that the developed approach could be very useful for recovering incomplete samples in a dataset instead of discarding them, mainly in situations where experiments are challenging.

Identification of lithofacies using Kohonen self-organizing maps

The immune system is the primary barrier to parasite infection, replication, and transmission following exposure, and variation in immunity can accordingly manifest in heterogeneity in traits that govern population-level infectious disease dynamics. While much work in ecoimmunology has focused on individual-level determinants of host immune defense (e.g., reproductive status and body condition), an ongoing challenge remains to understand the broader evolutionary and ecological contexts of this variation (e.g., phylogenetic relatedness and landscape heterogeneity) and to connect these differences into epidemiological frameworks. Ultimately, such efforts could illuminate general principles about the drivers of host defense and improve predictions and control of infectious disease. Here, we highlight recent work that synthesizes the complex drivers of immunological variation across biological scales of organization and scales these within-host differences to population-level infection outcomes. Such studies note the limitations involved in making species-level comparisons of immune phenotypes, stress the importance of spatial scale for immunology research, showcase several statistical tools for translating within-host data into epidemiological parameters, and provide theoretical frameworks for linking within- and between-host scales of infection processes. Building from these studies, we highlight several promising avenues for continued work, including the application of machine learning tools and phylogenetically controlled meta-analyses to immunology data and quantifying the joint spatial and temporal dependencies in immune defense using range expansions as model systems. We also emphasize the use of organismal traits (e.g., host tolerance, competence, and resistance) as a way to interlink various scales of analysis. Such continued collaboration and disciplinary cross-talk among ecoimmunology, disease ecology, and mathematical modeling will facilitate an improved understanding of the multi-scale drivers and consequences of variation in host defense.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual ‘fingerprints’ that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Application of machine learning tools for prediction of fertilizers/pesticides/dye adsorption onto agricultural waste-derived biochar/modified biochar for clean environment

The main goal of this paper is to present the application of structural sums, mathematical objects originating from the computational materials science, in construction of a feature space vector of two-dimensional random composites simulated by distributions of non-overlapping discs on the plane. Construction of the feature vector enables the immediate application of machine learning tools and data analysis techniques to random structures. In order to present the accuracy and the potential of structural sums as geometry descriptors, we apply them to classification problems comprising composites with circular inclusions as well as composites with shapes formed by discs. As an application, we perform the analysis of different models of composites in order to formulate the irregularity measure of random structures. We also visualize the relationship between the effective conductivity of two-dimensional composites and the geometry of inclusions.