Gut microbiota and artificial intelligence approaches: A scoping review

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances

A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naive Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3-G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS

Visual Attention for Significantly Influencing the Perception of Virtual Environments

The Human Visual System (HVS) is a key part of the rendering pipeline. The human eye is only capable of sensing image detail in a 2 foveal region, relying on rapid eye movements, or saccades, to jump between points of interest. These points of interest are prioritised based on the saliency of the objects in the scene or the task the user is performing. These ”glimpses” of a scene are then assembled by the HVS into a coherent, but inevitably imperfect, visual perception of the environment. In this process, much detail, which the HVS deems unimportant, may literally go unnoticed. In this paper we use knowledge of the HVS to influence what our attention is attracted to in computer graphics imagery, and thus what we actually perceive in those images. We influence the affinity of subjects towards an object based on the complexity of the context that object is put into. The images are rendered using the Radiance lighting simulation system. In this way, we are able to significantly influence users’ preferences in an e-commerce application. Detailed psychophysical studies are used to validate our approach

Perceptually guided high-fidelity rendering exploiting movement bias in visual attention

A major obstacle for real-time rendering of high-fidelity graphics is computational complexity. A key point to consider in the pursuit of "realism in real time" in computer graphics is that the Human Visual System (HVS) is a fundamental part of the rendering pipeline. The human eye is only capable of sensing image detail in a 2 degrees foveal region, relying on rapid eye movements, or saccades, to jump between points of interest. These points of interest are prioritized based on the saliency of the objects in the scene or the task the user is performing. Such "glimpses" of a scene are then assembled by the HVS into a coherent, but inevitably imperfect, visual perception of the environment. In this process, much detail, that the HVS deems unimportant, may literally go unnoticed. Visual science research has identified that movement in the background of a scene may substantially influence how subjects perceive foreground objects. Furthermore, recent computer graphics work has shown that both fixed viewpoint and dynamic scenes can be selectively rendered without any perceptual loss of quality, in a significantly reduced time, by exploiting knowledge of any high-saliency movement that may be present. A high-saliency movement can be generated in a scene if an otherwise static objects starts moving. In this article, we investigate, through psychophysical experiments, including eye-tracking, the perception of rendering quality in dynamic complex scenes based on the introduction of a moving object in a scene. Two types of object movement are investigated: (i) rotation in place and (ii) rotation combined with translation. These were chosen as the simplest movement types. Future studies may include movement with varied acceleration. The object's geometry and location in the scene are not salient. We then use this information to guide our high-fidelity selective renderer to produce perceptually high-quality images at significantly reduced computation times. We also show how these results can have important implications for virtual environment and computer games applications

Selective rendering in a multi-modal environment

Visual perception is becoming increasingly important in computer graphics. Research on human visual perception has led to the development of perception driven computer graphics techniques, where knowledge of the human visual system and, in particular, its weaknesses are exploited when rendering and displaying 3D graphics. It is well known that many sensory stimuli, including smell, may influence the amount of cognitive resources available to a viewer to perform a visual task. In this paper we investigate the influence smell effects have on the perception of object quality in a rendered image. We show how we can potentially accelerate the rendering of images by directing the viewer's attention towards the source of a smell and selectively rendering at high quality only the smell emitting objects. Other parts of an image can be rendered at a lower quality without the viewer being aware of this quality difference. By doing this, we can significantly reduce rendering time without any loss in the user's perception of delivered quality