A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

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 computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.

Download Full-text

Machine learning in medicine: a practical introduction to natural language processing

BMC Medical Research Methodology ◽

10.1186/s12874-021-01347-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Conrad J. Harrison ◽

Chris J. Sidey-Gibbons

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Latent Dirichlet Allocation ◽

Mental Health Problems ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector

Abstract Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text into interpretable datasets that can be analysed by statistical and machine learning (ML) models. The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software. Methods We performed three NLP experiments using publicly-available data obtained from medicine review websites. First, we conducted lexicon-based sentiment analysis on open-text patient reviews of four drugs: Levothyroxine, Viagra, Oseltamivir and Apixaban. Next, we used unsupervised ML (latent Dirichlet allocation, LDA) to identify similar drugs in the dataset, based solely on their reviews. Finally, we developed three supervised ML algorithms to predict whether a drug review was associated with a positive or negative rating. These algorithms were: a regularised logistic regression, a support vector machine (SVM), and an artificial neural network (ANN). We compared the performance of these algorithms in terms of classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results Levothyroxine and Viagra were reviewed with a higher proportion of positive sentiments than Oseltamivir and Apixaban. One of the three LDA clusters clearly represented drugs used to treat mental health problems. A common theme suggested by this cluster was drugs taking weeks or months to work. Another cluster clearly represented drugs used as contraceptives. Supervised machine learning algorithms predicted positive or negative drug ratings with classification accuracies ranging from 0.664, 95% CI [0.608, 0.716] for the regularised regression to 0.720, 95% CI [0.664,0.776] for the SVM. Conclusions In this paper, we present a conceptual overview of common techniques used to analyse large volumes of text, and provide reproducible code that can be readily applied to other research studies using open-source software.

Download Full-text

Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

Translational Stroke Research ◽

10.1007/s12975-021-00937-x ◽

2021 ◽

Author(s):

Kazutaka Uchida ◽

Junichi Kouno ◽

Shinichi Yoshimura ◽

Norito Kinjo ◽

Fumihiro Sakakibara ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forests ◽

Prediction Models ◽

Characteristic Curve ◽

Predictive Performance ◽

Vessel Occlusion ◽

Predictive Values ◽

Training Cohort ◽

Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

Download Full-text

Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01352-2 ◽

2020 ◽

Vol 20 (S11) ◽

Cited By ~ 1

Author(s):

Jacqueline Peng ◽

Mengge Zhao ◽

James Havrilla ◽

Cong Liu ◽

Chunhua Weng ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Clinical Manifestations ◽

Predictive Performance ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Research Articles

Abstract Background Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations. Methods We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score. Results We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP. Conclusion The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.

Download Full-text

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Cancers ◽

10.3390/cancers14020277 ◽

2022 ◽

Vol 14 (2) ◽

pp. 277

Author(s):

Zuzanna Anna Magnuska ◽

Benjamin Theek ◽

Milita Darguzyte ◽

Moritz Palmowski ◽

Elmar Stickeler ◽

...

Keyword(s):

Breast Lesion ◽

Characteristic Curve ◽

Cancer Diagnostics ◽

Lesion Detection ◽

Machine Learning Algorithms ◽

Classification Model ◽

P Value ◽

Future Design ◽

Sensitivity Specificity ◽

Derivatives Of

Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

Download Full-text

A Comparative Analysis of Machine Learning Techniques for Spam Detection

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1308 ◽

2021 ◽

pp. 657-661

Author(s):

Rashida Ali ◽

Ibrahim Rampurawala ◽

Mayuri Wandhe ◽

Ruchika Shrikhande ◽

Arpita Bhatkar

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Comparative Analysis ◽

Natural Language ◽

Language Processing ◽

High Volume ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Spam Detection ◽

Learning Techniques

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.

Download Full-text

Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽

10.3390/iot1020014 ◽

2020 ◽

Vol 1 (2) ◽

pp. 218-239 ◽

Cited By ~ 2

Author(s):

Ravikumar Patel ◽

Kalpdrum Passi

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Random Forest ◽

Natural Language ◽

Language Processing ◽

Machine Learning Algorithms ◽

World Cup ◽

Part Of Speech ◽

Twitter Data ◽

Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

Download Full-text

Computerized Answer Grading

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35044 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 618-619

Author(s):

Anurag Langan

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Computer Technology ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Grade Student ◽

Processing Techniques

Grading student answers is a tedious and time-consuming task. A study had found that almost on average around 25% of a teacher's time is spent in scoring the answer sheets of students. This time could be utilized in much better ways if computer technology could be used to score answers. This system will aim to grade student answers using the various Natural Language processing techniques and Machine Learning algorithms available today.

Download Full-text

Automated Essay Scoring using Ontology Generator and Natural Language Processing with Question Generator based on Blooms Taxonomy’s Cognitive Level

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9974.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2448-2457

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Ridge Regression ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Lasso Regression ◽

Automated Essay Scoring ◽

Essay Scoring

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.

Download Full-text

Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

JAMIA Open ◽

10.1093/jamiaopen/ooy061 ◽

2019 ◽

Vol 2 (1) ◽

pp. 139-149 ◽

Cited By ~ 9

Author(s):

Meijian Guan ◽

Samuel Cho ◽

Robin Petro ◽

Wei Zhang ◽

Boris Pasche ◽

...

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Cancer Patients ◽

Language Processing ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Free Text ◽

Treatment Change ◽

Progress Notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

Download Full-text