Answer Extraction for Definition Questions using Information Gain and Machine Learning

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q2Loo = 0.83, R2 = 0.87, Q2ext = 0.67, and R2ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.

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Prediction of Skin Diseases Using Machine Learning

10.4018/978-1-7998-7888-9.ch008 ◽

2022 ◽

pp. 154-178

Author(s):

Siddhartha Kumar Arjaria ◽

Vikas Raj ◽

Sunil Kumar ◽

Priyanshu Shrivastava ◽

Monu Kumar ◽

...

Keyword(s):

Machine Learning ◽

Data Mining ◽

Skin Disease ◽

Skin Diseases ◽

Information Gain ◽

Machine Learning Algorithms ◽

Ensemble Method ◽

Chi Square ◽

Data Mining Techniques ◽

Disease Rates

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

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Age-specific survival in prostate cancer using machine learning

Data Technologies and Applications ◽

10.1108/dta-10-2019-0189 ◽

2020 ◽

Vol 54 (2) ◽

pp. 215-234

Author(s):

M.N. Doja ◽

Ishleen Kaur ◽

Tanvir Ahmad

Keyword(s):

Prostate Cancer ◽

Machine Learning ◽

Metastatic Prostate Cancer ◽

Information Gain ◽

Age Groups ◽

Weighted Average ◽

Age Group ◽

Content Type ◽

Complete Dataset ◽

Average Accuracy

PurposeThe incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.Design/methodology/approachData of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.FindingsThe ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.Originality/valueThe study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.

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Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

International Journal of Molecular Sciences ◽

10.3390/ijms21155280 ◽

2020 ◽

Vol 21 (15) ◽

pp. 5280

Author(s):

Irini Furxhi ◽

Finbarr Murphy

Keyword(s):

Machine Learning ◽

Goodness Of Fit ◽

Information Gain ◽

Sampling Technique ◽

Exposure Dose ◽

Classification Model ◽

Machine Learning Techniques ◽

Data Set ◽

Tissue Specific

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

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Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach

Cancers ◽

10.3390/cancers11030431 ◽

2019 ◽

Vol 11 (3) ◽

pp. 431 ◽

Cited By ~ 11

Author(s):

Oneeb Rehman ◽

Hanqi Zhuang ◽

Ali Muhamed Ali ◽

Ali Ibrahim ◽

Zhongwei Li

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Information Gain ◽

Support Vector ◽

Learning Approach ◽

Breast Cancers ◽

Functional Studies ◽

Normal Tissues ◽

Machine Learning Approach ◽

Chi Squared

Certain small noncoding microRNAs (miRNAs) are differentially expressed in normal tissues and cancers, which makes them great candidates for biomarkers for cancer. Previously, a selected subset of miRNAs has been experimentally verified to be linked to breast cancer. In this paper, we validated the importance of these miRNAs using a machine learning approach on miRNA expression data. We performed feature selection, using Information Gain (IG), Chi-Squared (CHI2) and Least Absolute Shrinkage and Selection Operation (LASSO), on the set of these relevant miRNAs to rank them by importance. We then performed cancer classification using these miRNAs as features using Random Forest (RF) and Support Vector Machine (SVM) classifiers. Our results demonstrated that the miRNAs ranked higher by our analysis had higher classifier performance. Performance becomes lower as the rank of the miRNA decreases, confirming that these miRNAs had different degrees of importance as biomarkers. Furthermore, we discovered that using a minimum of three miRNAs as biomarkers for breast cancers can be as effective as using the entire set of 1800 miRNAs. This work suggests that machine learning is a useful tool for functional studies of miRNAs for cancer detection and diagnosis.

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Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data

Algorithms ◽

10.3390/a11110177 ◽

2018 ◽

Vol 11 (11) ◽

pp. 177 ◽

Cited By ~ 2

Author(s):

Xuedong Gao ◽

Minghan Yang

Keyword(s):

Machine Learning ◽

Categorical Data ◽

Data Clustering ◽

Information Gain ◽

Clustering Algorithms ◽

Number Of Clusters ◽

Cluster Compactness ◽

Clustering Validation ◽

Categorical Data Clustering

Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and can act as the objective function of clustering algorithms. In this paper, we first studied several well-known internal CVIs for categorical data clustering, and proved the ineffectiveness of evaluating the partitions of different numbers of clusters without any inter-cluster separation measures or assumptions; the accurateness of separation, along with its coordination with the intra-cluster compactness measures, can notably affect performance. Then, aiming to enhance the internal clustering validation measurement, we proposed a new internal CVI—clustering utility based on the averaged information gain of isolating each cluster (CUBAGE)—which measures both the compactness and the separation of the partition. The experimental results supported our findings with regard to the existing internal CVIs, and showed that the proposed CUBAGE outperforms other internal CVIs with or without a pre-known number of clusters.

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To use or not to use: Feature selection for sentiment analysis of highly imbalanced data

Natural Language Engineering ◽

10.1017/s1351324917000298 ◽

2017 ◽

Vol 24 (1) ◽

pp. 3-37 ◽

Cited By ~ 5

Author(s):

SANDRA KÜBLER ◽

CAN LIU ◽

ZEESHAN ALI SAYYED

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Sentiment Analysis ◽

Information Gain ◽

Binary Classification ◽

Small Subset ◽

Large Set ◽

Learning Approaches ◽

Selection Methods ◽

Data Set

AbstractWe investigate feature selection methods for machine learning approaches in sentiment analysis. More specifically, we use data from the cooking platform Epicurious and attempt to predict ratings for recipes based on user reviews. In machine learning approaches to such tasks, it is a common approach to use word or part-of-speech n-grams. This results in a large set of features, out of which only a small subset may be good indicators for the sentiment. One of the questions we investigate concerns the extension of feature selection methods from a binary classification setting to a multi-class problem. We show that an inherently multi-class approach, multi-class information gain, outperforms ensembles of binary methods. We also investigate how to mitigate the effects of extreme skewing in our data set by making our features more robust and by using review and recipe sampling. We show that over-sampling is the best method for boosting performance on the minority classes, but it also results in a severe drop in overall accuracy of at least 6 per cent points.

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Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

Scientific Reports ◽

10.1038/s41598-019-53656-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Rana Zia Ur Rehman ◽

Silvia Del Din ◽

Yu Guan ◽

Alison J. Yarnall ◽

Jian Qing Shi ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Clinical Decision Making ◽

Information Gain ◽

Step Length ◽

Support Vector ◽

Step Width ◽

Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

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A Study of Cross-National Differences in Happiness Factors Using Machine Learning Approach

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194015710023 ◽

2015 ◽

Vol 25 (09n10) ◽

pp. 1699-1702 ◽

Cited By ~ 2

Author(s):

Theresia Ratih Dewi Saputri ◽

Seok-Won Lee

Keyword(s):

Machine Learning ◽

Information Gain ◽

Development Project ◽

Support Vector ◽

National Differences ◽

Dimensionality Reduction Technique ◽

Machine Learning Approach ◽

National Happiness ◽

Using Data ◽

Cross National

National happiness has been actively studied throughout the past years. The happiness factor varies due to different human perspectives. The factors used in this work include both physical needs and the mental needs of humanity, for example, the educational factor. This work identified more than 90 features that can be used to predict the country happiness. Due to numerous features, it is unwise to rely on the prediction of national happiness by manual analysis. Therefore, this work used a machine learning technique called Support Vector Machine (SVM) to learn and predict the country happiness. In order to improve the prediction accuracy, dimensionality reduction technique which is the information gain was also used in this work. This technique was chosen due to its ability to explore the interrelationships among a set of variables. Using data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

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