Using Principal Paths to Walk Through Music and Visual Art Style Spaces Induced by Convolutional Neural Networks

AbstractComputational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights

Spathial: an R package for the evolutionary analysis of biological data

Abstract Summary A primary problem in high-throughput genomics experiments is finding the most important genes involved in biological processes (e.g. tumor progression). In this applications note, we introduce spathial, an R package for navigating high-dimensional data spaces. spathial implements the Principal Path algorithm, which is a topological method for locally navigating on the data manifold. The package, together with the core algorithm, provides several high-level functions for interpreting the results. One of the analyses we propose is the extraction of the genes that are mainly involved in the progress from one state to another. We show a possible application in the context of tumor progression using RNA-Seq and single-cell datasets, and we compare our results with two commonly used algorithms, edgeR and monocle3, respectively. Availability and implementation The R package spathial is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/spathial/index.html) and on GitHub (https://github.com/erikagardini/spathial). It is distributed under the GNU General Public License (version 3). Supplementary information Supplementary data are available at Bioinformatics online

Scenari applicativi per Smart Home su piattaforma Home Manager: verso il livello intelligence

Analisi degli scenari applicativi in ambiente Home Manager e progettazione, implementazione e collaudo di alcune delle funzionalità proposte

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains

Role of SIRT-3, p-mTOR and HIF-1\u3b1 in Hepatocellular Carcinoma Patients Affected by Metabolic Dysfunctions and in Chronic Treatment with Metformin

The incidence of hepatocellular carcinoma deriving from metabolic dysfunctions has increased in the last years. Sirtuin- (SIRT-3), phospho-mammalian target of rapamycin (p-mTOR) and hypoxia-inducible factor- (HIF-1\u3b1) are involved in metabolism and cancer. However, their role in hepatocellular carcinoma (HCC) metabolism, drug resistance and progression remains unclear. This study aimed to better clarify the biological and clinical function of these markers in HCC patients, in relation to the presence of metabolic alterations, metformin therapy and clinical outcome. A total of 70 HCC patients were enrolled: 48 and 22 of whom were in early stage and advanced stage, respectively. The expression levels of the three markers were assessed by immunohistochemistry and summarized using descriptive statistics. SIRT-3 expression was higher in diabetic than non-diabetic patients, and in metformin-treated than insulin-treated patients. Interestingly, p-mTOR was higher in patients with metabolic syndrome than those with different etiology, and, similar to SIRT-3, in metformin-treated than insulin-treated patients. Moreover, our results describe a slight, albeit not significant, benefit of high SIRT-3 and a significant benefit of high nuclear HIF-1\u3b1 expression in early-stage patients, whereas high levels of p-mTOR correlated with worse prognosis in advanced-stage patients. Our study highlighted the involvement of SIRT-3 and p-mTOR in metabolic dysfunctions that occur in HCC patients, and suggested SIRT-3 and HIF-1\u3b1 as predictors of prognosis in early-stage HCC patients, and p-mTOR as target for the treatment of advanced-stage HCC

Human Activity Recognition in the context of Health Smart Home by using Machine Learning Classification Models

Dal 2050, la percentuale di persone con età superiore a 65 anni sarà più del triplo. Di conseguenza, le persone affette da patologie legate all’età aumenteranno. Il tema di ricerca di questa tesi riguarda il riconoscimento di attività svolte dalle persone anziane nella propria abitazione (Human Activity Recognition – HAR) al fine di diagnosticare possibili patologie il prima possibile e di identificare situazioni anomale. Due datasets esistenti e liberamente accessibili sono stati utilizzati a tal scopo. In particolare, questi dataset contengono informazioni riguardanti le attività eseguite da diverse persone (rispettivamente 12 e 30) ed acquisite mediante molteplici sensori come sensori di movimento (posizione), sensori sugli sportelli (per monitorare l’utilizzo dell’armadio e dei pensili della cucina), sensore di temperatura ed umidità ed accelerometro. Queste informazioni sono poi state utilizzate per la classificazione delle attività di basso livello (quattro attività: sedersi, alzarsi, camminare, stendersi) e delle attività di alto livello (sei attività: dormire, mangiare, riposarsi, uso dei servizi igienici, uso del wc, vestirsi/svestirsi). La classificazione è stata realizzata utilizzando differenti tecniche di Machine Learning, quali Support Vector Machine, Artificial Neural Networks, K-Nearest Neighbours e Random Forest. Per validare gli algoritmi di classificazione sono state utilizzate tecniche di cross-validation, come K-fold e leave-one-out

Probabilistic Pocket Druggability Prediction via One-Class Learning

The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed via NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling

Using Principal Paths to Walk Through Music and Visual Art Style Spaces Induced by Convolutional Neural Networks

Computational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights