Othman Soufan

• Professor (Assistant) at St. Francis Xavier University

Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal b...

Radiation’s harmful effects on biological organisms have long been studied through mainly evaluating pathological changes in cells, tissues, or organs. Recently, there have been more accessible gene expression datasets relating to radiation exposure studies. This provides an opportunity to analyze responses at the molecular level toward revealing p...

Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Longitudinal b...

Chemical risk assessment for avian species typically depends on information from toxicity tests performed in adult birds. Early‐life stage (ELS) toxicity tests have been proposed as an alternative, but incorporation of these data into existing frameworks will require knowledge about the similarities/differences between ELS and adult responses. The...

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interact...

Current ecotoxicity testing programs are impeded as they predominantly rely on slow and expensive animal tests measuring adverse outcomes. Therefore, new approach methodologies (NAMs) increasingly involve short-term mechanistic assays that employ molecular endpoints to predict adverse outcomes of regulatory relevance. This study aimed to elucidate...

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, led to a global health crisis, with more than 157 mil-lion cases confirmed infected by May 2021. Effective medicationis desperately needed. Predicting drug-target interaction (DTI) isan important step to discover novel uses of chemical structures.Here, we develop a pipeline to predict novel DTI...

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. These interactions can create unexpected adverse pharmacological effects. By contrast, particular foods can aid in the recovery process of a patient. Towards characterizing the nature of food’s influence on pharmaco...

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI pro...

There is increasing pressure to develop alternative ecotoxicological risk assessment approaches that do not rely on expensive, time-consuming, and ethically questionable live animal testing. This study aimed to develop a comprehensive early life stage toxicity pathway model for the exposure of fish to estrogenic chemicals that is rooted in mechanis...

Motivation: Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that follows this approach is computationally inefficient and must be locally installed. Results: Fas...

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primaril...

Traditional results from toxicogenomics studies are complex lists of significantly impacted genes or gene sets, which are challenging to synthesize down to actionable results with a clear interpretation. Here we defined two sets of 21 custom gene sets, called the functional and statistical EcoToxModules, in fathead minnow (Pimephales promelas) to 1...

The use of toxicogenomic endpoints and increased reliance on early-life stage (ELS) animal exposures are two strategies that have been proposed to improve toxicity testing for regulatory risk assessment. However, it is unknown whether transcriptomic measures in ELS organisms are predictive of those measured in their adult counterparts. The present...

Incorporation of toxicogenomics into ecological risk assessment requires a good understanding of the genes/pathways that are commonly affected by exposure to toxic chemicals. Studies comparing transcriptomic responses across environmental chemicals are available for fish but less research has been done for birds. The present study aims to reveal un...

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets ha...

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets hav...

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets hav...

The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for ge...

Grass carp (Ctenopharyngodon idellus) is one of the most common aquaculture fish species around the world. High stocking density applied in the intensive C. idellus culture may have negative impact on fish health and flesh quality. To investigate the effect of stocking density on growth, flesh quality, and immune response of C. idellus, we conducte...

MOTIVATION: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. RESULTS: Here, we introduce a machine learning method with chemical fingerprint-based features for the prediction of the Gibbs free energy of biochemical reacti...

Birds are sensitive indicators of ecosystem health, but avian toxicity data for many chemicals of environmental relevance are limited. The overall goal of our research project is to develop early-life stage (ELS) avian toxicity tests for rapidly screening chemicals of ecological and regulatory concern. Here, we use our recently developed standardiz...

High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the v...

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have b...

Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms hav...

Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, meth...

Background: Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for...

DRABAL: Novel Method for Mining Large High-throughput Screening Assays using Bayesian Active Learning

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and freq...

Docking output results for Carbinoxamine, Granisetron, Ondansetron, Zalepon, Sitagliptin, Forasartan, Tasosartan, Udenafil, Tyloxapol with TSHR. The orange color highlights the top docking results of a drug binding to the chosen activation site. (TIFF)

Extended comparison of existing and proposed methods including an analysis of significance of difference between the reported performance metrics. (DOCX)

Effect of feature selection results on classification performance. (DOCX)

Details about the existing state-of-the-art solutions used in the study and their input parameters. The file includes also all information about DRAMOTE and its procedure. (DOCX)

Detailed comparison results for each dataset. Mean and variance of 5-fold cross-validation performance scores are displayed for each method and for each used classifiers. (DOCX)

Summary description of features generated for chemical compounds. The file also includes most of the features we selected after applying variable selection over the originals set of generated features. (DOCX)

Detailed docking scores including the set of random selected drugs and description of the docking procedure. (DOCX)

Extended literature review of the top predicted FDA drugs for the TSHR in humans. (DOCX)

A list of the top ranked prediction by DRAMOTE for potential drugs interacting with 17β-HSD10 in humans. (DOCX)

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features....

This paper presents a comparative study of automatic classification of different types of heart beat arrhythmias. The heart beats are classified into normal, premature ventricular contraction, atrial premature, right bundle branch block and left bundle branch block classes. Different classifiers are used in this work, namely support vector machine,...

The aim of this paper is to propose an application of mutual information-based ensemble methods to the analysis and classification of heart beats associated with different types of Arrhythmia. Models of multilayer perceptrons, support vector machines, and radial basis function neural networks were trained and tested using the MIT-BIH arrhythmia dat...