PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

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Deep Regressor Stacking for Air Ticket Prices Prediction

10.5753/sbsi.2017.6022 ◽

2017 ◽

Cited By ~ 6

Author(s):

Everton Santana ◽

Saulo Mastelini ◽

Sylvio Jr.

Keyword(s):

State Of The Art ◽

Real Life ◽

The Other ◽

Support Vector ◽

Price Prediction ◽

Single Target ◽

Novel Method ◽

Prediction Techniques ◽

Ticket Prices ◽

Over Time

Purchasing air tickets by the lowest price is a challenging task for consumers since the prices might fluctuate over time influenced by several factors. In order to support users’ decision, some price prediction techniques have been developed. Considering that this problem could be solved by multi-target approaches from Machine Learning, this work proposes a novel method looking forward to obtaining an improvement in air ticket prices prediction. The method, called Deep Regressor Stacking (DRS), applies a naive deep learning methodology to reach more accurate predictions. To evaluate the contribution of the DRS, it was compared with the competence of the single-target regression and two state-of-the-art multi-target regressions (Stacked Single Target and Ensemble of Regressor Chains). All four approaches were performed based on Random Forest and Support Vector Machine algorithms over two real-life airfares datasets. After results, it was concluded DRS outperformed the other three methods, being the most indicated (most predictive) to assist air passengers in the prediction of flight ticket price.

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The Prediction of Calpain Cleavage Sites with the mRMR and IFS Approaches

Mathematical Problems in Engineering ◽

10.1155/2013/861269 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Wenyi Zhang ◽

Xin Xu ◽

Longjia Jia ◽

Zhiqiang Ma ◽

Na Luo ◽

...

Keyword(s):

Molecular Mechanisms ◽

Solvent Accessibility ◽

Limited Proteolysis ◽

Cysteine Proteases ◽

Biochemical Properties ◽

Support Vector ◽

Cleavage Sites ◽

Future Direction ◽

User Friendly ◽

Better Than

Calpains are an important family of the Ca2+-dependent cysteine proteases which catalyze the limited proteolysis of many specific substrates. Calpains play crucial roles in basic physiological and pathological processes, and identification of the calpain cleavage sites may facilitate the understanding of the molecular mechanisms and biological function. But traditional experiment approaches to predict the sites are accurate, and are always labor-intensive and time-consuming. Thus, it is common to see that computational methods receive increasing attention due to their convenience and fast speed in recent years. In this study, we develop a new predictor based on the support vector machine (SVM) with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). And we concern the feature of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility to represent the calpain cleavage sites. Experimental results show that the performance of our predictor is better than several other state-of- the-art predictors, whose average prediction accuracy is 79.49%, sensitivity is 62.31%, and specificity is 88.12%. Since user-friendly and publicly accessible web servers represent the future direction for developing practically more useful predictors, here we have provided a web-server for the method presented in this paper.

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Prediction of Neddylation Sites Using the Composition of k-spaced Amino Acid Pairs and Fuzzy SVM

Current Bioinformatics ◽

10.2174/1574893614666191114123453 ◽

2020 ◽

Vol 15 (7) ◽

pp. 725-731

Author(s):

Zhe Ju ◽

Shi-Yun Wang

Keyword(s):

Amino Acid ◽

Molecular Mechanisms ◽

Feature Selection Method ◽

Class Imbalance ◽

Support Vector ◽

Post Translational Modification ◽

Fuzzy Support Vector Machine ◽

Accurate Identification ◽

Isopeptide Bonds ◽

User Friendly

Introduction: Neddylation is the process of ubiquitin-like protein NEDD8 attaching substrate lysine via isopeptide bonds. As a highly dynamic and reversible post-translational modification, lysine neddylation has been found to be involved in various biological processes and closely associated with many diseases. Objective: The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As traditional experimental methods are often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods: In this study, a novel predictor named CKSAAP_NeddSite is developed to detect neddylation sites. An effective feature encoding technology, the composition of k-spaced amino acid pairs, is used to encode neddylation sites. And the F-score feature selection method is adopted to remove the redundant features. Moreover, a fuzzy support vector machine algorithm is employed to overcome the class imbalance and noise problem. Results: As illustrated by 10-fold cross-validation, CKSAAP_NeddSite achieves an AUC of 0.9848. Independent tests also show that CKSAAP_NeddSite significantly outperforms existing neddylation sites predictor. Therefore, CKSAAP_NeddSite can be a useful bioinformatics tool for the prediction of neddylation sites. Feature analysis shows that some residues around neddylation sites may play an important role in the prediction. Conclusion: The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of neddylation. A user-friendly web-server for CKSAAP_NeddSite is established at 123.206.31.171/CKSAAP_NeddSite.

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Boosted Prediction of Antihypertensive Peptides Using Deep Learning

Applied Sciences ◽

10.3390/app11052316 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2316

Author(s):

Anum Rauf ◽

Aqsa Kiran ◽

Malik Tahir Hassan ◽

Sajid Mahmood ◽

Ghulam Mustafa ◽

...

Keyword(s):

Deep Learning ◽

In Silico ◽

Bioactive Peptides ◽

State Of The Art ◽

Peptide Sequence ◽

Antihypertensive Activity ◽

Support Vector ◽

Dipeptide Composition ◽

Independent Dataset ◽

Antihypertensive Peptides

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets.

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Synthesizing Conjunctive & Disjunctive Linear Invariants by K-means++ and SVM

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/6/3 ◽

2020 ◽

Vol 17 (6) ◽

pp. 847-856

Author(s):

Shengbing Ren ◽

Xiang Zhang

Keyword(s):

Software Verification ◽

State Of The Art ◽

Positive Sample ◽

Machine Learning Algorithms ◽

Support Vector ◽

Hoare Logic ◽

Excellent Performance ◽

Automated Software ◽

Inductive Invariants ◽

Linear Invariants

The problem of synthesizing adequate inductive invariants lies at the heart of automated software verification. The state-of-the-art machine learning algorithms for synthesizing invariants have gradually shown its excellent performance. However, synthesizing disjunctive invariants is a difficult task. In this paper, we propose a method k++ Support Vector Machine (SVM) integrating k-means++ and SVM to synthesize conjunctive and disjunctive invariants. At first, given a program, we start with executing the program to collect program states. Next, k++SVM adopts k-means++ to cluster the positive samples and then applies SVM to distinguish each positive sample cluster from all negative samples to synthesize the candidate invariants. Finally, a set of theories founded on Hoare logic are adopted to check whether the candidate invariants are true invariants. If the candidate invariants fail the check, we should sample more states and repeat our algorithm. The experimental results show that k++SVM is compatible with the algorithms for Intersection Of Half-space (IOH) and more efficient than the tool of Interproc. Furthermore, it is shown that our method can synthesize conjunctive and disjunctive invariants automatically

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A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180925100355 ◽

2019 ◽

Vol 15 (3) ◽

pp. 206-211 ◽

Cited By ~ 2

Author(s):

Jihui Tang ◽

Jie Ning ◽

Xiaoyan Liu ◽

Baoming Wu ◽

Rongfeng Hu

Keyword(s):

Machine Learning ◽

Amino Acid ◽

Amino Acid Position ◽

Cell Penetrating Peptides ◽

Support Vector ◽

Cell Penetration ◽

Drug Candidates ◽

Machine Learning Model ◽

Cell Penetrating ◽

Novel Method

Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.

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COVID-19 infection map generation and detection from chest X-ray images

Health Information Science and Systems ◽

10.1007/s13755-021-00146-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Aysen Degerli ◽

Mete Ahishali ◽

Mehmet Yamac ◽

Serkan Kiranyaz ◽

Muhammad E. H. Chowdhury ◽

...

Keyword(s):

State Of The Art ◽

Ground Truth ◽

Clinical Use ◽

X Ray ◽

Learning Techniques ◽

Map Generation ◽

Severity Grading ◽

Chest X Ray ◽

Novel Method ◽

Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

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ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3418214 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-23

Author(s):

Mingliang Xu ◽

Qingfeng Li ◽

Jianwei Niu ◽

Hao Su ◽

Xiting Liu ◽

...

Keyword(s):

State Of The Art ◽

Visual Quality ◽

Qr Code ◽

Quick Response ◽

Estimation Model ◽

Qr Codes ◽

Excellent Performance ◽

Novel Method ◽

Coarse To Fine

Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.

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A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽

10.3390/data6080087 ◽

2021 ◽

Vol 6 (8) ◽

pp. 87

Author(s):

Sara Ferreira ◽

Mário Antunes ◽

Manuel E. Correia

Keyword(s):

Machine Learning ◽

Digital Forensics ◽

State Of The Art ◽

Forensic Analysis ◽

Third Party ◽

Support Vector ◽

Multimedia Content ◽

Digital Forensic ◽

Video Frames ◽

Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

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TRAF6-IRF5 kinetics, TRIF, and biophysical factors drive synergistic innate responses to particle-mediated MPLA-CpG co-presentation

Science Advances ◽

10.1126/sciadv.abd4235 ◽

2021 ◽

Vol 7 (3) ◽

pp. eabd4235

Author(s):

P. Pradhan ◽

R. Toy ◽

N. Jhita ◽

A. Atalis ◽

B. Pandey ◽

...

Keyword(s):

Immune Responses ◽

Molecular Mechanisms ◽

Lipid A ◽

Critical Role ◽

Mouse Bone Marrow ◽

Biophysical Properties ◽

Gm Csf ◽

Monophosphoryl Lipid A ◽

Integrated Response ◽

Particulate Carriers

Innate immune responses to pathogens are driven by co-presentation of multiple pathogen-associated molecular patterns (PAMPs). Combinations of PAMPs can trigger synergistic immune responses, but the underlying molecular mechanisms of synergy are poorly understood. Here, we used synthetic particulate carriers co-loaded with monophosphoryl lipid A (MPLA) and CpG as pathogen-like particles (PLPs) to dissect the signaling pathways responsible for dual adjuvant immune responses. PLP-based co-delivery of MPLA and CpG to GM-CSF–driven mouse bone marrow–derived antigen-presenting cells (BM-APCs) elicited synergistic interferon-β (IFN-β) and interleukin-12p70 (IL-12p70) responses, which were strongly influenced by the biophysical properties of PLPs. Mechanistically, we found that MyD88 and interferon regulatory factor 5 (IRF5) were necessary for IFN-β and IL-12p70 production, while TRIF signaling was required for the synergistic response. Both the kinetics and magnitude of downstream TRAF6 and IRF5 signaling drove the synergy. These results identify the key mechanisms of synergistic Toll-like receptor 4 (TLR4)–TLR9 co-signaling in mouse BM-APCs and underscore the critical role of signaling kinetics and biophysical properties on the integrated response to combination adjuvants.

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