Gianvito UrgesePolitecnico di Torino | polito · DIST - Interuniversity Department of Regional and Urban Studies and Planning
Gianvito Urgese
PhD of Engineering
About
49
Publications
8,523
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1,163
Citations
Citations since 2017
Introduction
His research interests focus on: i) Research and design of optimised task-specific algorithms; ii) Development of tools for the study of non-coding biological sequences; iii) Design of heterogeneous SW/HW architectures to accelerate bioinformatics algorithms; iv) Design of algorithms for exploiting the computational advantages of neuromorphic platforms; v) Definition of an alternative engineering process reference architecture for the management of a digitalised life-cycle of IoT solutions.
Additional affiliations
March 2016 - December 2016
October 2011 - August 2012
Publications
Publications (49)
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic compu...
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic...
The evolution of industrial digitalisation has accelerated in recent years with the availability of hyperconnectivity, low-cost miniaturised electronic components, edge computing, and Internet of Things (IoT) technologies. More generally, with these key enablers, the concept of a system of systems (SoS) is becoming a reality in the industry domain....
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world applications. Recent deep learning approaches have reached outstanding accuracy in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic...
Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analy...
Background
The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and...
We describe an update of MirGeneDB, the manually curated microRNA gene database. Adhering to uniform and consistent criteria for microRNA annotation and nomenclature, we substantially expanded MirGeneDB with 30 additional species representing previously missing metazoan phyla such as sponges, jellyfish, rotifers and flatworms. MirGeneDB 2.1 now con...
Background
Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) seque...
SpiNNaker is a neuromorphic hardware platform, especially designed for the simulation of Spiking Neural Networks (SNNs). To this end, the platform features massively parallel computation and an efficient communication infrastructure based on the transmission of small packets. The effectiveness of SpiNNaker in the parallel execution of the PageRank...
Advancements around the modern digital industry gave birth to a number of closely interrelated concepts: in the age of the Internet of Things (IoT), System of Systems (SoS), Cyber-Physical Systems (CPS), Digital Twins and the fourth industrial revolution, everything revolves around the issue of designing well-understood, sound and secure complex sy...
One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, t...
The application of Artificial Intelligence is becoming common in many engineering fields. Among them, one of the newest and rapidly evolving is software generation, where AI can be used to automatically optimise the implementation of an algorithm for a given computing platform. In particular, Deep Learning technologies can be used to the decide how...
In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts...
Motivation:
High-Throughput Next-Generation-Sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically...
Predicting the oncogenic potential of a gene fusion transcript is an important and challenging task in the study of cancer development. To this date, the available approaches mostly rely on protein domain analysis to provide a probability score explaining the oncogenic potential of a gene fusion. In this paper, a Convolutional Neural Network model...
SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transm...
Motivation:
MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparison...
In this paper, we propose a methodology for efficiently mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a Spiking Neural Network (SNN) in real-time. The problem of neuron-to-core mapping is relevant as a non-efficient allocation may impact real-time and reliabil...
Modern heterogeneous platforms require compilers capable of choosing the appropriate device for the execution of program portions. This paper presents a machine learning method designed for supporting mapping decisions through the analysis of the program source code represented in LLVM assembly language (IR) for exploiting the advantages offered by...
The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration a...
The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we pr...
Gene fusions have a very important role in the study of cancer development. In this regard, predicting the probability of protein fusion transcripts of developing into a cancer is a very challenging and yet not fully explored research problem. To this date, all the available approaches in literature try to explain the oncogenic potential of gene fu...
Neuromorphic architectures are emerging not only for real-time simulation of brain-scale biological neural networks but also to support innovative brain-inspired computational paradigms. In both domains there is an increasing demand for flexibility in terms of network configuration and runtime redesign of network parameters and simulated neurons mo...
Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case,...
Background: MicroRNAs (miRNAs) are small RNA molecules (~22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, a lack of consensus on miRNA/isomiR analyses exists, and the...
Background:
The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome Venter et al. (2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, ow...
In this paper we present a new Partitioning and Placement methodology able to maps Spiking Neural Network on parallel neuromorphic platforms. This methodology improves scalability/reliability of Spiking Neural Network (SNN) simulations on many-core and densely interconnected platforms. SNNs mimic brain activity by emulating spikes sent between neur...
Massive parallel sequencing of transcriptomes, revealed the presence of many miRNAs and miRNAs variants named isomiRs with a potential role in several cellular processes through their interaction with a target mRNA. Many methods and tools have been recently devised to detect and quantify miRNAs from sequencing data. However, all of them are impleme...
In this paper we present a top-down methodology aimed at evaluating the scalability of Spiking Neural Network (SNN) simulations on massively many-core and densely interconnected platforms. Spiking neural networks mimic brain activity by emulating spikes sent among neurons populations. Many-core platforms are emerging computing targets to achieve re...
Protein comparison is gaining importance year after year since it has been demonstrated that biologists can find correlation between different species, or genetic mutations that can lead to cancer and genetic diseases. Protein sequence alignment is the most computational intensive task when performing protein comparison. To speed-up alignment, dedi...
In recent years Field-Coupled devices, like Quantum dot Cellular Automata, are gaining an ever increasing attention from the scientific community. The computational paradigm beyond this device topology is based on the interaction among neighbor cells to propagate information through circuits. Among the various implementations of this theoretical pr...
The advent of Next Generation Sequencing (NGS) technology has enabled a new major approach for micro RNAs (miRNAs) expression profiling through the so called RNA-Sequencing (RNA-Seq).
Different tools have been developed in the last years in order to detect and quantify miRNAs, especially in pathological samples, starting from the big amount of data...
Smith Waterman algorithm (S-W) is a widespread method to perform local alignments of biological sequences of proteins, DNA and RNA molecules. Indeed, S-W is able to ensure better accuracy levels with respect to the heuristic alignment algorithms by extensively exploring all the possible alignment configurations between the sequences under examinati...
Biosequence alignment recently received an amazing
support from both commodity and dedicated hardware plat-
forms. The limitless requirements of this application motivate
the search for improved implementations to boost processing
time and capabilities. We propose an unprecedented hardware
improvement to the classic Smith-Waterman (S-W) algorithm
b...
Projects
Projects (3)
The new emerging generation of microsystems represents a major opportunity for a substantial EU economic growth, in an industry counting already more than 200.000 workers and a turnover in 2019 of €450 billion. The Mesomorph concept provides the means to overcome the following 4 main hurdles:
1. The intrinsic physic of microsystems doesn’t allow the simple downsizing of conventional technologies to industrialize micromanufacturing processes. Mesomorphallows to limit the number of micromanipulation tasks by integrating an all-in-onemachine featuring novel processes for the direct creation of functions (electronic, fluidic, optic) directly on a substrate, with a RESOLUTION down to 300nm, by combining multi-material addition (Two-Photon Polymerization, Atomic Layer 3D nano printing) and subtraction (Femtolaser micro-ablation) in a self-contained white room.
2. Because of the intrinsic slowness of physical processes at microscale, productivity cannot be achieved by sequencing multiple single steps. Mesomorph proposes a scaleup throughput by PARALLELIZATION and batch processing UP TO 50k PARTS/YEAR, leveraging a new multiple micronozzles system to extend the SADALP working area from 10x10mm up to 500x500mm, and concurrently leveraging on the beam splitting technique of a high-power fs laser for ablation.
3. Microsystems cannot be conceived with subcomponents. Mesomorph includes a specific Design-to-Lifevalue Platform to guide the development of new microsystems by fully exploiting the new processes.
4. Innovation cannot be limited by the financial risk associated with the necessary investments. Mesomorph implements a new “Manufacturing as a Service” business model in which all the value chain’s actors can benefit from a positive net cash flow since production's start, effectively removing entry barriers for innovators.
Mesomorph consortium is composed of 13 partners from 5 different countries. Each partner represents excellence in its own field.
isomiRs are miRNA variation sequences. It has been proven the real existence and function of these new molecules. Still, since their discovery in 2010, there is no standard format to describe them.
I am leading a community open project to work in a first draft for a systematic nomenclature.
Join if you like to participate: https://mirtop.github.io
