Search results for "machine learning."
showing 10 items of 1455 documents
Computer-Aided Diagnosis for Prostate Cancer using Multi-Parametric Magnetic Resonance Imaging
2016
Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world.CaP growth is characterized by two main types of evolution: (i) the slow-growing tumours progress slowly and usually remain confined to the prostate gland; (ii) the fast-growing tumours metastasize from prostate gland to other organs, which might lead to incurable diseases.Therefore, early diagnosis and risk assessment play major roles in patient treatment and follow-up.In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexit…
Getting Docking into Shape Using Negative Image-Based Rescoring
2019
The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in the structure-based drug discovery. To remedy this problem, elaborate rescoring and post-processing schemes have been developed with a varying degree of success, specificity, and cost. The negative imagebased rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets.The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein’s ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is dir…
Experimental Evaluation of Protein Secondary Structure Predictors
2009
Understanding protein biological function is a key issue in modern biology, which is largely determined by its 3D shape. Protein 3D shape, in its turn, is functionally implied by its amino acid sequence. Since the direct inspection of such 3D structures is rather expensive and time consuming, a number of software techniques have been developed in the last few years that predict a spatial model, either of the secondary or of the tertiary form, for a given target protein starting from its amino acid sequence. This paper offers a comparison of several available automatic secondary structure prediction tools. The comparison is of the experimental kind, where two relevant sets of proteins, a non…
Identification of Trans-Golgi Network Proteins in Arabidopsis thaliana Root Tissue
2014
Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along densit…
A Proposed Knowledge Based Approach for Solving Proteomics Issues
2010
In this paper we present a novel knowledge-based approach that aims at helping scientists to face and resolve a large number of proteomics problem. The system architecture is based on an ontology to model the knowledge base, a reasoner that starting from the user's request and a set of rules builds the workflow of tasks to be done, and an executor that runs the algorithms and software scheduled by the reasoner. The system can interact with the user showing him intermediate results and several options in order to refine the workflow and supporting him to choose among different forks. Thanks to the presence of the knowledge base and the modularity provided by the ontology, the system can be e…
Design of a Neural Network Model as a Decision Making Aid in Renal Transplant
2004
This paper presents the application of this new tool of data processing in the study of the problem that arises when a renal transplant is indicated for a paediatric patient. Its aim is the development and validation of a neural network based model which can predict the success of the transplant over the short, medium and long term, using pre-operative characteristics of the patient (recipient) and implant organ (donor). When compared to results of logistic regression, the results of the proposed model showed better performance. Once the model is obtained, it will be converted into a tool for predicting the efficiency of the transplant protocol in order to optimise the donor-recipient pair …
Prelogical Test: An Alternative Tool for Early Detection of Learning Difficulties
2014
Abstract Difficulties during the preschool age commonly lead to children who cannot solve problems, organize information and create meaning. It is necessary to predict factors that may affect their future learning. The aim is to develop an evaluation tool, to be applied in groups and that can easily evaluate results, to detect future learning problems in children of 3-6 years old. Computational intelligence techniques could contribute greatly to analyze results and to detect patterns that otherwise would not be apparent. Two protocols were implemented: an Indirect Variables Protocol (IVP) which captures children's personal data, and a Direct Variables Protocol (DVP) that assesses the graphi…
Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study
2021
AbstractRecent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrode’s montage. Here, we delineate how a closed-loop tES-fMRI study of …
Deformed quons and bi-coherent states
2017
We discuss how a q-mutation relation can be deformed replacing a pair of conjugate operators with two other and unrelated operators, as it is done in the construction of pseudo-fermions, pseudo-bosons and truncated pseudo-bosons. This deformation involves interesting mathematical problems and suggests possible applications to pseudo-hermitian quantum mechanics. We construct bi-coherent states associated to $\D$-pseudo-quons, and we show that they share many of their properties with ordinary coherent states. In particular, we find conditions for these states to exist, to be eigenstates of suitable annihilation operators and to give rise to a resolution of the identity. Two examples are discu…
Domain Generation Algorithm Detection Using Machine Learning Methods
2018
A botnet is a network of private computers infected with malicious software and controlled as a group without the knowledge of the owners. Botnets are used by cybercriminals for various malicious activities, such as stealing sensitive data, sending spam, launching Distributed Denial of Service (DDoS) attacks, etc. A Command and Control (C&C) server sends commands to the compromised hosts to execute those malicious activities. In order to avoid detection, recent botnets such as Conficker, Zeus, and Cryptolocker apply a technique called Domain-Fluxing or Domain Name Generation Algorithms (DGA), in which the infected bot periodically generates and tries to resolve a large number of pseudorando…