Matthews correlation coefficient
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Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric Open
Data imbalance is frequently encountered in biomedical applications. Resampling techniques can be used in binary classification to tackle this issue. However such solutions are not desired when the number of samples in the small class is l…
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DeepSynergy: predicting anti-cancer drug synergy with Deep Learning Open
Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerge…
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Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers Open
Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction i…
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An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure Open
About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improv…
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Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost Open
The advance in technologies such as e-commerce and financial technology (FinTech) applications have sparked an increase in the number of online card transactions that occur on a daily basis. As a result, there has been a spike in credit ca…
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Automatic Diagnosis of Rice Diseases Using Deep Learning Open
Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special…
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Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction Open
Many chemicals that disrupt endocrine function have been linked to a variety of adverse biological outcomes. However, screening for endocrine disruption using in vitro or in vivo approaches is costly and time-consuming. Computational metho…
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A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping Open
Landslides are highly hazardous geological disasters that can potentially threaten the safety of human life and property. As a result, landslide susceptibility mapping (LSM) plays an important role in the landslide prevention system. Recen…
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mCSM–NA: predicting the effects of mutations on protein–nucleic acids interactions Open
Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light…
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Sequence‐based prediction of protein–peptide binding sites using support vector machine Open
Protein–peptide interactions are essential for all cellular processes including DNA repair, replication, gene‐expression, and metabolism. As most protein – peptide interactions are uncharacterized, it is cost effective to investigate them …
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Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient Open
The accuracy of a classification is fundamental to its interpretation, use and ultimately decision making. Unfortunately, the apparent accuracy assessed can differ greatly from the true accuracy. Mis-estimation of classification accuracy m…
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PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions Open
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, …
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Prostate Cancer Detection Using Deep Learning and Traditional Techniques Open
Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readi…
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Structure-based prediction of protein– peptide binding regions using Random Forest Open
Motivation Protein–peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellula…
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Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2 Open
Significance SARS-CoV-2 infection proceeds through the binding of viral surface spike protein to the human ACE2 protein. The global spread of the infection has led to the emergence of fitter and more transmissible variants with increased a…
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Advanced Feature-Selection-Based Hybrid Ensemble Learning Algorithms for Network Intrusion Detection Systems Open
As cyber-attacks become remarkably sophisticated, effective Intrusion Detection Systems (IDSs) are needed to monitor computer resources and to provide alerts regarding unusual or suspicious behavior. Despite using several machine learning …
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ATPbind: Accurate Protein–ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons Open
Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. Howev…
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ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides Open
Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore …
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Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus Open
Background Coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome. SARS-CoV-2 with potential origin of bat is still circulating in China. In this study, a prediction model is proposed to evaluate the…
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Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams Open
Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dr…
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PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network Open
Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technol…
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A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification Open
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computationa…
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On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach Open
DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acid…
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Improving Sequence-Based Prediction of Protein–Peptide Binding Residues by Introducing Intrinsic Disorder and a Consensus Method Open
Protein-peptide interaction is crucial for many cellular processes. It is difficult to determine the interaction by experiments as peptides are often very flexible in structure. Accurate sequence-based prediction of peptide-binding residue…
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Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning Open
The accurate predicting of physical properties and bioactivity of drug molecules in deep learning depends on how molecules are represented. Many types of molecular descriptors have been developed for quantitative structure-activity/propert…
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Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein–Ligand Scoring Functions Open
Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, b…
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iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks Open
Background Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in t…
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Pred-binding: large-scale protein–ligand binding affinity prediction Open
Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins…
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Prediction of Protein–ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm Open
Accurately identifying protein–ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict…
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Classification of plant diseases using machine and deep learning Open
Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the gr…