An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. METHODS: In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. RESULTS: Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. CONCLUSION: This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

Identifying quantitative sncRNAs signature using global sequencing as a potential biomarker for tuberculosis diagnosis and their role in regulating host response.

OBJECTIVES: The study aimed to identify a quantitative signature of circulating small non-coding RNAs (sncRNAs) as a biomarker for pulmonary tuberculosis disease (active-TB/ATB) and explore their regulatory roles in host-pathogen interactions and disease progression. METHODS: We conducted a cross-sectional study recruiting subjects diagnosed with active-TB (drug-sensitive and drug-resistant) and healthy controls. Sera samples were collected and utilized for preparing small RNA libraries. Quantitative patterns of circulating sncRNAs (miRNAs, piRNAs and tRFs) were identified via high-throughput sequencing and DeSeq2 analysis and validated in independent active-TB cohorts. Functional knockdown for two selected miRNAs were also performed. RESULTS: A diagnostic signature of four sncRNAs for both drug-sensitive and drug-resistant active-TB cases was validated, exhibiting an AUC of 0.96 (95% CI: 0.937-0.996, p < 0.001) with 86.7% sensitivity (95% CI: 0.775-0.932) and 91.7% specificity (95% CI: 0.730-0.990) in ROC analysis. Functional knockdown demonstrated regulatory roles of hsa-miR-223-5p and hsa-miR-10b-5p in Mycobacterium tuberculosis (Mtb) growth and pro-inflammatory cytokine expression (IL-6 and IL-8). CONCLUSION: The study identified a diagnostic tool utilizing a signature of four sncRNAs with high specificity and sensitivity, enhancing our understanding of sncRNAs as ATB diagnostic biomarker. Additionally, hsa-miR-223-5p and hsa-miR-10b-5p demonstrated potential roles in Mtb pathogenesis and host-response to infection.

Annotation of nuclear lncRNAs based on chromatin interactions.

The human genome is pervasively transcribed and produces a wide variety of long non-coding RNAs (lncRNAs), constituting the majority of transcripts across human cell types. Some specific nuclear lncRNAs have been shown to be important regulatory components acting locally. As RNA-chromatin interaction and Hi-C chromatin conformation data showed that chromatin interactions of nuclear lncRNAs are determined by the local chromatin 3D conformation, we used Hi-C data to identify potential target genes of lncRNAs. RNA-protein interaction data suggested that nuclear lncRNAs act as scaffolds to recruit regulatory proteins to target promoters and enhancers. Nuclear lncRNAs may therefore play a role in directing regulatory factors to locations spatially close to the lncRNA gene. We provide the analysis results through an interactive visualization web portal at https://fantom.gsc.riken.jp/zenbu/reports/#F6_3D_lncRNA.

DPI_CDF: druggable protein identifier using cascade deep forest.

BACKGROUND: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory. METHODS: In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. RESULTS: The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY: The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Unified mRNA Subcellular Localization Predictor based on machine learning techniques.

BACKGROUND: The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. METHODS: In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). RESULTS: The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. AVAILABILITY: We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids.

Methicillin-resistant Staphylococcus aureus (MRSA) is a growing concern for human lives worldwide. Anti-MRSA peptides act as potential antibiotic agents and play significant role to combat MRSA infection. Traditional laboratory-based methods for annotating Anti-MRSA peptides are although precise but quite challenging, costly, and time-consuming. Therefore, computational methods capable of identifying Anti-MRSA peptides accelerate the drug designing process for treating bacterial infections. In this study, we developed a novel sequence-based predictor "iMRSAPred" for screening Anti-MRSA peptides by incorporating energy estimation and physiochemical and sequential information. We successfully resolved the skewed imbalance phenomena by using synthetic minority oversampling technique plus Tomek link (SMOTETomek) algorithm. Furthermore, the Shapley additive explanation method was leveraged to analyze the impact of top-ranked features in the prediction task. We evaluated multiple machine learning algorithms, i.e., CatBoost, Cascade Deep Forest, Kernel and Tree Boosting, support vector machine, and HistGBoost classifiers by 10-fold cross-validation and independent testing. The proposed iMRSAPred method significantly improved the overall performance in terms of accuracy and Matthew's correlation coefficient (MCC) by 5.45 and 0.083%, respectively, on the training data set. On the independent data set, iMRSAPred improved accuracy and MCC by 3.98 and 0.055%, respectively. We believe that the proposed method would be useful in large-scale Anti-MRSA peptide prediction and provide insights into other bioactive peptides.

EMO-MoviNet: Enhancing Action Recognition in Videos with EvoNorm, Mish Activation, and Optimal Frame Selection for Efficient Mobile Deployment.

The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept. The Mobile Video Network (MoviNet) is a member of the memory-efficient architectures discovered through Neural Architecture Search (NAS), which balances accuracy and efficiency by integrating spatial, temporal, and spatio-temporal operations. Our research implements the MoviNet model on the UCF101 and HMDB51 datasets, pre-trained on the kinetics dataset. Upon implementation on the UCF101 dataset, a generalization gap was observed, with the model performing better on the training set than on the testing set. To address this issue, we replaced batch normalization with EvoNorm, which unifies normalization and activation functions. Another area that required improvement was key-frame selection. We also developed a novel technique called Optimal Frame Selection (OFS) to identify key-frames within videos more effectively than random or densely frame selection methods. Combining OFS with Mish nonlinearity resulted in a 0.8-1% improvement in accuracy in our UCF101 20-classes experiment. The EMO-MoviNet-A2* model consumes 86% fewer FLOPs and approximately 90% fewer parameters on the UCF101 dataset, with a trade-off of 1-2% accuracy. Additionally, it achieves 5-7% higher accuracy on the HMDB51 dataset while requiring seven times fewer FLOPs and ten times fewer parameters compared to the reference model, Motion-Augmented RGB Stream (MARS).

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners.

Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players' physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players' performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders' role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15-30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.

AI in drug discovery and its clinical relevance.

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

An Overview of Metabolomics Studies Based on Qatari Population.

The aim of metabolomics research is to identify the metabolites that play a role in various biological traits and diseases. This scoping review provides an overview of the current state of metabolomics studies that focus on the Qatari population. Our findings indicate that few studies have been conducted on this population, with a focus on diabetes, dyslipidemia, and cardiovascular disease. Blood samples were the primary source of metabolite identification, and several potential biomarkers for these diseases were proposed. To the best of our knowledge, this is the first scoping review that presents an overview of metabolomics studies in Qatar.

Revolutionizing Healthcare with Foundation AI Models.

ChatGPT is a foundation Artificial Intelligence (AI) model that has opened up new opportunities in digital healthcare. Particularly, it can serve as a co-pilot tool for doctors in the interpretation, summarization, and completion of reports. Furthermore, it can build upon the ability to access the large literature and knowledge on the internet. So, chatGPT could generate acceptable responses for the medical examination. Hence. It offers the possibility of enhancing healthcare accessibility, expandability, and effectiveness. Nonetheless, chatGPT is vulnerable to inaccuracies, false information, and bias. This paper briefly describes the potential of Foundation AI models to transform future healthcare by presenting ChatGPT as an example tool.

Deep Learning in Colorectal Cancer Classification: A Scoping Review.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and its diagnosis and classification remain challenging for pathologists and imaging specialists. The use of artificial intelligence (AI) technology, specifically deep learning, has emerged as a potential solution to improve the accuracy and speed of classification while maintaining the quality of care. In this scoping review, we aimed to explore the utilization of deep learning for the classification of different types of colorectal cancer. We searched five databases and selected 45 studies that met our inclusion criteria. Our results show that deep learning models have been used to classify colorectal cancer using various types of data, with histopathology and endoscopy images being the most common. The majority of studies used CNN as their classification model. Our findings provide an overview of the current state of research on deep learning in the classification of colorectal cancer.

A Deep Learning-Based Approach Towards Simultaneous Localization of Optic Disc and Fovea from Retinal Fundus Images.

In this work, we propose a multi-task learning-based approach towards the localization of optic disc and fovea from human retinal fundus images using a deep learning-based approach. Formulating the task as an image-based regression problem, we propose a Densenet121-based architecture through an extensive set of experiments with a variety of CNN architectures. Our proposed approach achieved an average mean absolute error of only 13pixels (0.04%), mean squared error of 11 pixels (0.005%), and a root mean square error of only 0.02 (13%) on the IDRiD dataset.

Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning.

The optical disc in the human retina can reveal important information about a person's health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images. Using an attention-based residual U-Net, we showed that the optical disc in a human retina image can be detected with more than 99% pixel-level accuracy and around 95% in Matthew's Correlation Coefficient. A comparison with variants of UNet with different encoder CNN architectures ascertains the superiority of the proposed approach across multiple metrics.

Predicting Overall Survival in METABRIC Cohort Using Machine Learning.

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival. We achieved a slightly higher Concordance index than the state of art and identified biological pathways related to the top genes considered important by our model.

Using AI for Detection, Prediction and Classification of Retinal Detachment.

The current state of machine learning (ML) and deep learning (DL) algorithms used to detect, classify and predict the onset of retinal detachment (RD) were examined in this scoping review. This severe eye condition can cause vision loss if left untreated. By analyzing the medical imaging modalities such as fundus photography, AI could help to detect peripheral detachment at an earlier stage. We have searched five databases: PubMed, Google Scholar, ScienceDirect, Scopus, and IEEE. Two reviewers independently carried out the selection of the studies and their data extractions. 32 studies fulfilled our eligibility criteria from the 666 references collected. In particular, based on the performance metrics employed in these studies, this scoping review provides a general overview of emerging trends and practices concerning using ML and DL algorithms for detecting, classifying, and predicting RD.

The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review.

This scoping review explores the advantages and disadvantages of using ChatGPT in medical education. We searched PubMed, Google Scholar, Medline, Scopus, and Science Direct to identify relevant studies. Two reviewers independently conducted study selection and data extraction, followed by a narrative synthesis. Out of 197 references, 25 studies met the eligibility criteria. The primary applications of ChatGPT in medical education include automated scoring, teaching assistance, personalized learning, research assistance, quick access to information, generating case scenarios and exam questions, content creation for learning facilitation, and language translation. We also discuss the challenges and limitations of using ChatGPT in medical education, such as its inability to reason beyond existing knowledge, generation of incorrect information, bias, potential undermining of students' critical thinking skills, and ethical concerns. These concerns include using ChatGPT for exam and assignment cheating by students and researchers, as well as issues related to patients' privacy.

Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review.

Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.

Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techniques used for long-term prediction of T2DM and evaluate their performance, we conducted a scoping review using PRISMA-ScR. Of the 40 papers included in this review, 23 studies used Machine Learning (ML) as the most common AI technique, with Deep Learning (DL) models used exclusively in four studies. Of the 13 studies that used both ML and DL, 8 studies employed ensemble learning models, and SVM and RF were the most used individual classifiers. Our findings highlight the importance of accuracy and recall as validation metrics, with accuracy being used in 31 studies, followed by recall in 29 studies. These discoveries emphasize the critical role of high predictive accuracy and sensitivity in detecting positive T2DM cases.