527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

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 limited Moreover the use of inadequate performance metrics, such as accuracy, lead to poor generalization results because the classifiers tend to predict the largest size class One of the good approaches to deal with this issue is to optimize performance metrics that are designed to handle data imbalance Matthews Correlation Coefficient (MCC) is widely used in Bioinformatics as a performance metric We are interested in developing a new classifier based on the MCC metric to handle imbalanced data We derive an optimal Bayes classifier for the MCC metric using an approach based on Frechet derivative We show that the proposed algorithm has the nice theoretical property of consistency Using simulated data, we verify the correctness of our optimality result by searching in the space of all possible binary classifiers The proposed classifier is evaluated on 64 datasets from a wide range data imbalance We compare both classification performance and CPU efficiency for three classifiers: 1) the proposed algorithm (MCC-classifier), the Bayes classifier with a default threshold (MCC-base) and imbalanced SVM (SVM-imba) The experimental evaluation shows that MCC-classifier has a close performance to SVM-imba while being simpler and more efficient

/pdf/optimal-classifier-for-imbalanced-data-using-matthews-326t5v9ce2.pdf

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric.

N6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide acid at the nitrogen-6 position. Many conventional computational methods for identifying N6-methyladenosine sites are limited by the small amount of data available. Taking advantage of the thousands of m6A sites detected by high-throughput sequencing, it is now possible to discover the characteristics of m6A sequences using deep learning techniques. To the best of our knowledge, our work is the first attempt to use word embedding and deep neural networks for m6A prediction from mRNA sequences. Using four deep neural networks, we developed a model inferred from a larger sequence shifting window that can predict m6A accurately and robustly. Four prediction schemes were built with various RNA sequence representations and optimized convolutional neural networks. The soft voting results from the four deep networks were shown to outperform all of the state-of-the-art methods. We evaluated these predictors mentioned above on a rigorous independent test data set and proved that our proposed method outperforms the state-of-the-art predictors. The training, independent, and cross-species testing data sets are much larger than in previous studies, which could help to avoid the problem of overfitting. Furthermore, an online prediction web server implementing the four proposed predictors has been built and is available at http://server.malab.cn/Gene2vec/.

/pdf/gene2vec-gene-subsequence-embedding-for-prediction-of-1ey1tpv7xj.pdf

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA.

In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Similarity computation strategies in the microRNA-disease network: a survey

DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods. In this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot. Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA
 . In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.

/pdf/ndna-prot-identification-of-dna-binding-proteins-based-on-2o7o43rf7q.pdf

nDNA-prot: Identification of DNA-binding proteins based on unbalanced classification

Protein Folds Prediction with Hierarchical Structured SVM

Cancer Diagnosis Through IsomiR Expression with Machine Learning Method

An Empirical Study of Features Fusion Techniques for Protein-Protein Interaction Prediction

Pain is one of the most common symptoms of malignant peritoneal mesothelioma. Therefore, analgesia serves an indispensable role in the treatment of this condition. Morphine is a representative opioid, which is widely used in clinical practice; however, excessive or unreasonable application can cause poisoning. Few cases of morphine poisoning have been reported, and cases of morphine poisoning in patients with malignant peritoneal mesothelioma are even more rare. Here, we present a case of morphine poisoning in a patient with malignant peritoneal mesothelioma. The patient had a high abdominal tumor load, hepatorenal insufficiency, and was treated with a combination of morphine and the sedative benzodiazepine, eventually leading to morphine poisoning. Therefore, for cancer pain, omni-directional and whole-process management should be emphasized. In patients with hepatorenal insufficiency, those treated with morphine combined with benzodiazepines, or those with a high abdominal tumor load, attention should be paid to drug absorption, excretion and interaction, and the drug dose during administration should be reduced to avoid drug poisoning. If poisoning symptoms occur, timely measures should be taken to reduce poison absorption and increase poison excretion, and antagonists should be used to reverse the poisoning and reduce the damage caused.

https://www.spandidos-publications.com/10.3892/etm.2023.11896/download

Dapeng Li

Papers

nDNA-prot: Identification of DNA-binding proteins based on unbalanced classification

Protein Folds Prediction with Hierarchical Structured SVM

Cancer Diagnosis Through IsomiR Expression with Machine Learning Method

An Empirical Study of Features Fusion Techniques for Protein-Protein Interaction Prediction

Morphine poisoning in a patient with malignant peritoneal mesothelioma: A case report