BRMCF: Binary Relevance and MLSMOTE Based Computational Framework to Predict Drug Functions From Chemical and Biological Properties of Drugs.

In silico machine learning based prediction of drug functions considering the drug properties would substantially enhance the speed and reduce the cost of identifying promising drug leads. The drug function prediction capability of different drug properties happens to be different. So assessing these is advantageous in drug discovery. The task of drug function prediction is multi-label in nature reason being, in case of several drugs, multiple functions are associated with a drug. A number of existing works have ignored this inherent multi-label nature of the problem in context of addressing the issue of class imbalance. In the present work, a computational framework named as BRMCF has been proposed for analysing the prediction capability of chemical and biological properties of drugs toward drug functions in view of multi-label nature of problem. It employs Binary Relevance (BR) approach along with five base classifiers for handling the multi-label prediction task and MLSMOTE for addressing the issue of class imbalance. The proposed framework has been validated and compared with BR, Classifier Chains (CC) and Deep Neural Network (DNN) method on four drug properties datasets: SMILES Strings (SS) dataset, 17 Molecular Descriptors (17MD) dataset, Protein Sequences (PS) dataset and drug perturbed Gene EXpression Profiles (GEX) dataset. The analysis of results shows that the proposed framework BRMCF has outperformed BR, CC and DNN method in terms of exact match ratio, precision, recall, F1-score, ROC-AUC which signifies the effectiveness of MLSMOTE. Further, assessment of prediction capability of different drug properties is done and they are ranked as SS GEX PS 17MD. Additionally, the visualization and analysis of drug function co-occurrences signify the appropriateness of the proposed framework for drug function co-occurrence detection and in signaling the new possible drug leads where the detection rate varies from 94.34% to 99.61%.

Integrative analysis of chemical properties and functions of drugs for adverse drug reaction prediction based on multi-label deep neural network.

The prediction of adverse drug reactions (ADR) is an important step of drug discovery and design process. Different drug properties have been employed for ADR prediction but the prediction capability of drug properties and drug functions in integrated manner is yet to be explored. In the present work, a multi-label deep neural network and MLSMOTE based methodology has been proposed for ADR prediction. The proposed methodology has been applied on SMILES Strings data of drugs, 17 molecular descriptors data of drugs and drug functions data individually and in integrated manner for ADR prediction. The experimental results shows that the SMILES Strings + drug functions has outperformed other types of data with regards to ADR prediction capability.

Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data.

The classification of patients as cancer and normal patients by applying the computational methods on their gene expression profiles is an extremely important task. Recently, deep learning models, mainly multilayer perceptron and convolutional neural networks, have gained popularity for being applied on this type of datasets. This paper aims to analyze the performance of deep learning models on different types of cancer gene expression datasets as no such consolidated work is available. For this purpose, three deep learning models along with two feature selection method and four cancer gene expression datasets have been considered. It has resulted in a total of 24 different combinations to be analyzed. Out of four datasets, two are imbalanced and two are balanced in terms of number of normal and cancer samples. Experimental results show that the deep learning models have performed well in terms of true positive rate, precision, F1-score, and accuracy.

BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning.

Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred choice of radiologists because of its high sensitivity and specificity. In the present work, deep learning algorithm based model, BID-Net, has been proposed for the automation of bone invasion detection. BID-Net performs the binary classification of CT scan images as the images with bone invasion and images without bone invasion. The proposed BID-Net model has achieved an outstanding accuracy of 93.62%. The model is also compared with six Transfer Learning models like VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, ResNet-101 and BID-Net outperformed over the other models. As there exists no previous studies on bone invasion detection using Deep Learning models, so the results of the proposed model have been validated from the experts of practitioner radiologists, S.M.S. hospital, Jaipur, India.

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma.

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.

An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks.

With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from -20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.

Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions.

The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid approach has been presented for finding quantitative multidimensional association from the large amount of data. Firstly, it employs clustering technique for segmentation of data into semantically coherent clusters. Furthermore, disproportionality method called proportional reporting ratio is applied on clustered data for generating statistically strong associations. The performance of the proposed methodology has been examined on the data taken from the U.S. Food and Drug Administration Adverse Event Reporting System database corresponding to Aspirin and nine other drugs which are prescribed along with Aspirin. The experimental results show that the proposed approach discovered a number of association rules which are very comprehensive and informative regarding relationship of patient traits and drugs with adverse drug reactions. On comparing experimental results with LPMiner, it is observed that the quantitative association rules discovered by LPMiner are just 8.3% of what have been discovered by the proposed methodology.