Network-adjusted Kendall’s Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data

Motivation In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant feature screening difficult. Several independence screening methods have been developed, but they fail to account for gene–gene dependency information, and may be sensitive to outlying feature data. Results We improve the inverse probability-of-censoring weighted (IPCW) Kendall’s tau statistic by using Google’s PageRank Markov matrix to incorporate feature dependency network information. Also, to tackle outlying feature data, the nonparanormal approach transforming the feature data to multivariate normal variates are utilized in the graphical lasso procedure to estimate the network structure in feature data. Simulation studies under various scenarios show that the proposed network-adjusted weighted Kendall’s tau approach leads to more accurate feature selection and survival prediction than the methods without accounting for feature dependency network information and outlying feature data. The applications on the clinical survival outcome data of diffuse large B-cell lymphoma and of The Cancer Genome Atlas lung adenocarcinoma patients demonstrate clearly the advantages of the new proposal over the alternative methods. Supplementary information Supplementary data are available at Bioinformatics online.

Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation

Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.

Text Classification Using Self-Structure Extended Multinomial Naive Bayes

With the advent of neural networks and its subfields like deep neural networks and convolutional neural networks, it is possible to make text classification predictions with high accuracy. Among the many subtypes of naive Bayes, multinomial naive Bayes is used for text classification. Many attempts have been made to somehow develop an algorithm that uses the simplicity of multinomial naive Bayes and at the same time incorporates feature dependency. One such effort was put in structure extended multinomial naive Bayes, which uses one-dependence estimators to inculcate dependencies. Basically, one-dependence estimators take one of the attributes as features and all other attributes as its child. This chapter proposes self structure extended multinomial naïve Bayes, which presents a hybrid model, a combination of the multinomial naive Bayes and structure extended multinomial naive Bayes. Basically, it tries to classify the instances that were misclassified by structure extended multinomial naive Bayes as there was no direct dependency between attributes.

CNN Model With Hilbert Curve Representation of DNA Sequence For Enhancer Prediction

MotivationEnhancers are distal cis-acting regulating regions that play a vital role in gene transcription. However, due to the inherent nature of enhancers being linearly distant from the affected gene in an irregular manner while being spatially close at the same time, systematically predicting enhancers has been a challenging task. Although several computational predictor models through both epigenetic marker analysis and sequence-based analysis have been proposed, they lack generalization capacity across different enhancer datasets and have feature dependency. On the other hand, the recent proliferation of deep learning methods has opened previously unknown avenues of approach for sequence analysis tasks which eliminates feature dependency and achieves greater generalization. Therefore, harnessing the power of deep learning based sequence analysis techniques to develop a more generalized model than the ones developed before to predict enhancer region in a DNA sequence is a topic of interest in bioinformatics.ResultsIn this study, we develop the predictor model CHilEnPred that has been trained with the visual representation of the DNA sequences with Hilbert Curve. We report our computational prediction result on FANTOM5 dataset where CHilEnPred achieves an accuracy of 94.97% and AUC of 0.987 on test data.AvailabilityOur CHilEnPred model can be freely accessed at https://github.com/iatahmid/[email protected]