scholarly journals A highly accurate model for screening prostate cancer using propensity index panel of ten genes

2021 ◽  
Author(s):  
Shipra Jain ◽  
Kawal Preet Kaur Malhotra ◽  
Sumeet Patiyal ◽  
Gajendra P.S. Raghava
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Single Gene ◽  
Specific Antigen ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
New Approach ◽  
Learning Techniques ◽  
Feature Selection Techniques

Prostate-specific antigen (PSA) is a key biomarker, which is commonly used to screen patients of prostate cancer. There is a significant number of unnecessary biopsies that are performed every year, due to poor accuracy of PSA based biomarker. In this study, we identified alternate biomarkers based on gene expression that can be used to screen prostate cancer with high accuracy. All models were trained and test on gene expression profile of 500 prostate cancer and 51 normal samples. Numerous feature selection techniques have been used to identify potential biomarkers. These biomarkers have been used to develop various models using different machine learning techniques for predicting samples of prostate cancer. Our logistic regression-based model achieved highest AUROC 0.91 with accuracy 82.42% on validation dataset. We introduced a new approach called propensity index, where expression of gene is converted into propensity. Our propensity based approach improved the performance of classification models significantly and achieved AUROC 0.99 with accuracy 96.36% on validation dataset. We also identified and ranked selected genes which can be used to discriminate prostate cancer patients from health individuals with high accuracy. It was observed that single gene based biomarkers can only achieve accuracy around 90%. In this study, we got best performance using a panel of 10 genes; random forest model using propensity index.

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

scholarly journals A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

2019 ◽  
Vol 18 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Niddal Imam ◽  
Biju Issac ◽  
Seibu Mary Jacob
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Research Community ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Learning Approach ◽  
New Approach ◽  
Detection Systems ◽  
Learning Techniques ◽  
Over Time

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become very attractive to spammers. Unlike other types of spam, Twitter spam has become a serious issue in the last few years. The large number of users and the high amount of information being shared on Twitter play an important role in accelerating the spread of spam. In order to protect the users, Twitter and the research community have been developing different spam detection systems by applying different machine-learning techniques. However, a recent study showed that the current machine learning-based detection systems are not able to detect spam accurately because spam tweet characteristics vary over time. This issue is called “Twitter Spam Drift”. In this paper, a semi-supervised learning approach (SSLA) has been proposed to tackle this. The new approach uses the unlabeled data to learn the structure of the domain. Different experiments were performed on English and Arabic datasets to test and evaluate the proposed approach and the results show that the proposed SSLA can reduce the effect of Twitter spam drift and outperform the existing techniques.

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

scholarly journals The revolution of personalized psychiatry: will technology make it happen sooner?

2017 ◽  
Vol 48 (5) ◽  
pp. 705-713 ◽  
Author(s):  
G. Perna ◽  
M. Grassi ◽  
D. Caldirola ◽  
C. B. Nemeroff
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Time Data ◽  
New Approach ◽  
Technological Advances ◽  
Learning Techniques ◽  
Personalized Psychiatry ◽  
Smart Wearable

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Sensitivity and specificity of a whole-blood RNA transcript-based diagnostic test for the diagnosis of prostate cancer (CaP) compared with prostate-specific antigen (PSA) alone

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 5052-5052
Author(s):  
R. W. Ross ◽  
D. Bankaitis-Davis ◽  
L. Siconolfi ◽  
L. Katz ◽  
K. Storm ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Expression Analysis ◽  
Whole Blood ◽  
Specific Antigen ◽  
Gene Model ◽  
Healthy Men ◽  
Psa Testing ◽  
Rna Transcript

5052 Background: Screening for CaP with PSA testing is limited by a high number of false postives, particularly in the setting of benign prostatic hypertrophy (BPH). The goal of this study was to develop whole blood RNA transcript-based diagnostic tests that improve the diagnosis of CaP over PSA alone. Methods: From August 2006 to October 2008, three prospective cohorts of men consented to the collection of whole blood in PAXgene Blood RNA tubes for gene expression analysis: men with newly diagnosed, localized, untreated CaP, otherwise healthy men without CaP, and otherwise healthy men with BPH. 168 inflammation and CaP-related genes (Source MDx Precision Profiles) were assayed using optimized Q-PCR technology. Logistic regression methods were used to develop models to optimize prostate cancer diagnosis. Results: 182 men underwent expression analysis (n = 76, 76 and 30 for CaP, normal, and BPH cohorts, respectively). The CaP and normal cohorts were age matched (median age 60 yrs); the BPH cohort median age was 70. Considering only the CaP and normal cohorts, PSA alone (using a cut-off of 4 ng/ml) had a specificity of 94.7%, but sensitivity of only 71.1% for diagnosis of CaP, or 90.8% and 77.6%, respectively, when using age-adjusted PSA criteria. A model consisting of the expression analysis of 6 genes and PSA had a higher specificity (96.1%) and a much improved sensitivity (97.4%) for CaP diagnosis. When the BPH cohort was added, the improvement of the 6-gene model remained (sensitivity and specificity of 97.4% and 92.0% vs 77.6% and 88.1% using the age-adjusted PSA criteria). Further model development using the CaP and BPH cohorts yielded a 5-gene model which, integrated with PSA and age, correctly predicted 96.1% of the CaP pts and 93.3% of BPH pts. Conclusions: These results suggest that specific whole blood RNA transcript levels can assess abnormal gene expression associated with CaP. Such a molecular CaP biomarker would be a powerful tool to reduce unnecessary biopsies in patients without CaP and detect CaP in patients with PSA values below the current cutoff. Validation of these results is ongoing and will be available at the time of the meeting. [Table: see text]

miRNAs as novel biomarkers in the management of prostate cancer

2017 ◽  
Vol 55 (5) ◽  
Author(s):  
Xavier Filella ◽  
Laura Foj
Keyword(s):  
Prostate Cancer ◽  
Current Knowledge ◽  
Expression Patterns ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
New Approach ◽  
Novel Biomarkers ◽  
Non Coding Rnas ◽  
Underlying Mechanisms ◽  
New Biomarkers

AbstractmicroRNAs (miRNAs) are small non-coding RNAs that control gene expression posttranscriptionally and are part of the giant non codifying genoma. Cumulating data suggest that miRNAs are promising potential biomarkers for many diseases, including cancer. Prostate cancer (PCa) detection is currently based in the serum prostate-specific antigen biomarker and digital rectal examination. However, these methods are limited by a low predictive value and the adverse consequences associated with overdiagnosis and overtreatment. New biomarkers that could be used for PCa detection and prognosis are still needed. Recent studies have demonstrated that aberrant expressions of microRNAs are associated with the underlying mechanisms of PCa. This review attempts to extensively summarize the current knowledge of miRNA expression patterns, as well as their targets and involvement in PCa pathogenesis. We focused our review in the value of circulating and urine miRNAs as biomarkers in PCa patients, highlighting the existing discrepancies between different studies, probably associated with the important methodological issues related to their quantitation and normalization. The majority of studies have been performed in serum or plasma, but urine obtained after prostate massage appears as a new way to explore the usefulness of miRNAs. Large screening studies to select a miRNA profile have been completed, but bioinformatics tools appear as a new approach to select miRNAs that are relevant in PCa development. Promising preliminary results were published concerning miR-141, miR-375 and miR-21, but larger and prospective studies using standardized methodology are necessary to define the value of miRNAs in the detection and prognosis of PCa.

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