scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

scholarly journals EyeBallGUI: A Tool for Visual Inspection and Binary Marking of Multi-channel Bio-signals

2017 ◽  
Author(s):  
Kieran S. Mohr ◽  
Bahman Nasseroleslami ◽  
Parameswaran M. Iyer ◽  
Orla Hardiman ◽  
Edmund C. Lalor
Keyword(s):  
Visual Inspection ◽  
Binary Classification ◽  
Analysis Tool ◽  
Data Retention ◽  
Wide Range ◽  
Sample Data ◽  
Electroencephalogram Eeg ◽  
Free Open Source ◽  
Selection Of

AbstractA wide range of studies in human neuroscience rely on the analysis of electrophysiological bio-signals such as electroencephalogram (EEG) where customized data analysis may require supervised artefact rejection, binary marking through visual inspection, selection of noise and artefact samples for pre-processing algorithms, and selection of clinically-relevant signal segments in neurological conditions. Nevertheless, the existing preprocessing tools do not provide the needed flexibility to handle such tasks efficiently. We therefore developed a free open-source Graphical User Interface (GUI), EyeBallGUI, that allows visualization and flexible, manual marking (binary classification) of multi-channel bio-signal data. EyeBallGUI, developed for MATLAB®, allows the user to interactively and accurately inspect and mark multi-channel digitized data with no restriction on marking periods of data in subsets of channels (a restriction in place in existing tools). The new tool facilitates precise, manual marking of bio-signals by allowing any desired segment of data to be marked in any subset of channels. It is therefore of utility in circumstances where such flexibility is essential. The developed GUI is an auxiliary analysis tool that shall facilitate neural signal (pre-)processing applications where it is desirable to perform accurate supervised artefact rejection, flexible data marking for increased data retention yield, extraction of specific signal segments by expert users from sample data, or labeling of data for clinical and scientific research purposes.

scholarly journals Negative mpMRI Rules Out Extra-Prostatic Extension in Prostate Cancer Before Robot-Assisted Radical Prostatectomy

2022 ◽  
Author(s):  
Eoin Dinneen ◽  
Clare Allen ◽  
Tom Strange ◽  
Daniel Heffernan-Ho ◽  
Jelena Banjeglav ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Binary Classification ◽  
Characteristic Curve ◽  
Robot Assisted ◽  
Diagnostic Quality ◽  
Prostate Imaging ◽  
Likert Score ◽  
Operative Staging

The accuracy of multi-parametric MRI (mpMRI) in pre-operative staging of prostate cancer (PCa) remains controversial. Objective: To evaluate the ability of mpMRI to accurately predict PCa extra-prostatic extension (EPE) on a side-specific basis using a risk-stratified 5-point Likert scale. This study also aimed to assess the influence of mpMRI scan quality on diagnostic accuracy. Patients and Methods: We included 124 men who underwent robot-assisted RP (RARP) as part of the NeuroSAFE PROOF study at our centre. Three radiologists retrospectively reviewed mpMRI blinded to RP pathology and assigned a Likert score (1-5) for EPE on each side of the prostate. Each scan was also ascribed a Prostate Imaging Quality (PI-QUAL) score for assessing the quality of the mpMRI scan, where 1 represents poorest and 5 represents best diagnostic quality. Outcome measurements and statistical analyses: Diagnostic performance is presented for binary classification of EPE including 95% confidence intervals and area under the receiver operating characteristic curve (AUC). Results: A total of 231 lobes from 121 men (mean age 56.9 years) were evaluated. 39 men (32.2%), or 43 lobes (18.6%) had EPE. Likert score ≥3 had sensitivity (SE), specificity (SP), NPV, PPV of 90.4%, 52.3%, 96%, 29.9%, respectively, and AUC was 0.82 (95% CI: 0.77-0.86). AUC was 0.63 (95% CI: 0.37-0.9), 0.77 (0.71-0.84) and 0.92 (0.88-0.96) for biparametric scans, PI-QUAL 1-3 and PI-QUAL 4-5 scans, respectively. Conclusions: MRI can be used effectively by genitourinary radiologists to rule out EPE and help inform surgical planning for men undergoing RARP. EPE prediction was more reliable when the MRI scan was a) multi-parametric and b) of a higher image quality according to the PI-QUAL scoring system.

scholarly journals Protein Family Classification using Deep Learning

2018 ◽  
Author(s):  
K S Naveenkumar ◽  
Babu R Mohammed Harun ◽  
R Vinayakumar ◽  
KP Soman
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Protein Classification ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Deep Learning Algorithm ◽  
N Gram ◽  
Family Classification ◽  
Selection Of

AbstractProtein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

scholarly journals Photometric classification of Hyper Suprime-Cam transients using machine learning

2020 ◽  
Vol 72 (5) ◽  
Author(s):  
Ichiro Takahashi ◽  
Nao Suzuki ◽  
Naoki Yasuda ◽  
Akisato Kimura ◽  
Naonori Ueda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Full Dataset ◽  
Transient Data ◽  
Type Classification ◽  
Potential Use

Abstract The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes.

Binary Classification of Network-Generated Flow Data Using a Machine Learning Algorithm

2021 ◽  
Vol 15 (1) ◽  
pp. 26-43
Author(s):  
Sikha Bagui ◽  
Keenal M. Shah ◽  
Yizhi Hu ◽  
Subhash Bagui
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Information Gain ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Intrusion Detection Systems ◽  
Machine Learning Algorithm ◽  
Detection Systems ◽  
Data Environment

This study proposes a model for building intrusion detection systems. The dataset used, CICIDS 2017, contains 14 different attacks with 85 features for each attack. This high dimensionality of the data is a major challenge when building efficient intrusion detection systems, especially in today's big data environment, since a lot of the features are redundant. The main goal in this paper was to reduce the number of features and present a detailed discussion of the important features. For feature selection, information gain was used in an iterative way, and for classification, a machine learning algorithm, the J48 decision tree algorithm, was used. The important features for the classification of each attack were identified, and the features that were important for classifying multiple attacks were also identified and discussed.

scholarly journals Selection of aggregated classifiers for the prediction of the state of technical objects

2019 ◽  
pp. 361-367 ◽  
Author(s):  
D A Zhukov ◽  
V N Klyachkin ◽  
V R Krasheninnikov ◽  
Yu E Kuvayskova
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Bayes Classifier ◽  
Object State ◽  
Machine Learning Methods ◽  
State Identification ◽  
Healthy State ◽  
Selection Of

The basic data in the problem of the prediction of technical object’s state of health based on the known indicators of its operation are the known results of the object state estimation by information about previous service. The problem may be solved using the machine learning methods, it reduces to binary classification of states of the object. The research was conducted in the Matlab environment, ten various basic methods of machine learning were used: naive Bayes classifier, neural networks, bagging of decision trees and others. In order to improve quality of healthy state identification, it has been suggested that aggregated methods combining several basic classifiers should be used. This paper addresses the issue of selection of the best aggregated classifier. The effectiveness of such approach has been confirmed by numerous tests of real-world objects.

scholarly journals Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal

2021 ◽  
Vol 15 ◽  
Author(s):  
Kotaro Yamashiro ◽  
Jiayan Liu ◽  
Nobuyoshi Matsumoto ◽  
Yuji Ikegaya
Keyword(s):  
Deep Learning ◽  
Primary Motor Cortex ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Brain Regions ◽  
Acid Decarboxylase ◽  
Cell Type ◽  
Custom Made ◽  
Excitatory Neurons

Excitatory neurons and GABAergic interneurons constitute neural circuits and play important roles in information processing. In certain brain regions, such as the neocortex and the hippocampus, there are fewer interneurons than excitatory neurons. Interneurons have been quantified via immunohistochemistry, for example, for GAD67, an isoform of glutamic acid decarboxylase. Additionally, the expression level of other proteins varies among cell types. For example, NeuN, a commonly used marker protein for postmitotic neurons, is expressed differently across brain regions and cell classes. Thus, we asked whether GAD67-immunopositive neurons can be detected using the immunofluorescence signals of NeuN and the fluorescence signals of Nissl substances. To address this question, we stained neurons in layers 2/3 of the primary somatosensory cortex (S1) and the primary motor cortex (M1) of mice and manually labeled the neurons as either cell type using GAD67 immunosignals. We then sought to detect GAD67-positive neurons without GAD67 immunosignals using a custom-made deep learning-based algorithm. Using this deep learning-based model, we succeeded in the binary classification of the neurons using Nissl and NeuN signals without referring to the GAD67 signals. Furthermore, we confirmed that our deep learning-based method surpassed classic machine-learning methods in terms of binary classification performance. Combined with the visualization of the hidden layer of our deep learning algorithm, our model provides a new platform for identifying unbiased criteria for cell-type classification.

Use of photodynamic therapy for photodamage skin (review of literature)

2019 ◽  
Vol 1 (7) ◽  
pp. 19-23
Author(s):  
S. I. Surkichin ◽  
N. V. Gryazeva ◽  
L. S. Kholupova ◽  
N. V. Bochkova
Keyword(s):  
Photodynamic Therapy ◽  
Comparative Evaluation ◽  
Clinical Manifestations ◽  
Light Sources ◽  
Review Of Literature ◽  
Photosensitizing Agents ◽  
Selection Of

The article provides an overview of the use of photodynamic therapy for photodamage of the skin. The causes, pathogenesis and clinical manifestations of skin photodamage are considered. The definition, principle of action of photodynamic therapy, including the sources of light used, the classification of photosensitizers and their main characteristics are given. Analyzed studies that show the effectiveness and comparative evaluation in the selection of various light sources and photosensitizing agents for photodynamic therapy in patients with clinical manifestations of photodamage.

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