Medinformatics

A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

Rusul Ali Jabbar Alhatemi — 2023-12-27

Stroke ranks as one of the deadliest diseases globally, emphasizing the crucial need for early diagnosis. This study aims to create a two-stage classification system for stroke and non-stroke images to support early clinical detection. Deep learning, a cornerstone of diagnosis, detection, and prompt treatment, is the primary methodology. Transfer learning adapts successful deep learning architectures for diverse problems, and ensemble learning combines multiple classifiers for enhanced results. These two techniques are applied to classify stroke using a dataset of stroke and normal images. In the initial stage, six pre-trained models are fine-tuned, with DenseNet, Xception, and EfficientNetB2 emerging as the top performers, achieving validation accuracies of 98.4%, 98.4%, and 98%, respectively. These models serve as base learners within an ensemble framework. A weighted average ensemble method combines them, resulting in a remarkable 99.84% accuracy on a reserved test dataset. This approach exhibits promise for stroke detection, a life-threatening condition, while also demonstrating the effectiveness of ensemble techniques in enhancing model performance.

Received: 27 October 2023 | Revised: 24 November 2023 | Accepted: 19 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Since the data used in the study were removed from the Kaggle platform after the study was conducted, no data link is provided. However, the study data can be sent to researchers who request it, with privacy and ethical restrictions.

The Importance of KRAS Quantification for a Clinicopathological Characterization in Colorectal Cancer Patients

Alexandru Adrian Bratei — 2023-11-14

KRAS, a protein whose name stands for Kirsten rat sarcoma, is of practice important nowadays due to its implications in tumorigenesis and metastatic potential. In this paper, the levels of KRAS in colorectal cancer patients have been determined by using a stochastic method and the results have been searched for correlation with clinicopathological features. Patients with their clinical and pathological features were selected from the database of the project GRAPHSENSGASTROINTES, and used accordingly with the Ethics committee approval nr. 32647/2018 awarded by the County Emergency Hospital from Targu-Mures. There have been analysed 4 kinds of samples (whole blood, saliva, urine and tissue) by using a stochastic method with stochastic microsensors as screening tools. The results, consisting in levels of KRAS in all the four biological fluids (whole blood, saliva, urine and tissue), have been correlated with a large series of pathological features such as tumor location among the colon, the tumor dimensions and infiltration depth, gross appearance, budding, stroma features and blood vessels, lymphatic vessels and perineural invasion. By using KRAS levels in all the four biological fluids, the correlations with clinicopathological features can be extremely useful for the oncologist and the surgeon for a better management of patients. These results are not only extremely valuable, but they can also be obtained in just a couple hours, with a low cost and high accuracy.

Received: 21 August 2023 | Revised: 18 September 2023 | Accepted: 3 November 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Identification of Key Gene Modules and Novel Transcription Factors in Tetralogy of Fallot Using Machine Learning and Network Topological Features

Sona Charles — 2023-10-10

Tetralogy of Fallot (TOF) is a combinatorial congenital abnormality comprising of ventricular septal defect (VSD), pulmonary valve stenosis, a misplaced aorta and a thickened right ventricular wall. Biologically relevant module identification from transcriptome data may be considered as a binary classification problem. We utilized publicly accessible mRNA expression data to extract the differentially expressed genes (DEGs) and further weighted gene co-expression network analysis to identify ten modules in TOF. Network topological properties of modular and non-modular genes were considered as features for binary classification. We applied SVM, Random Forest, Decision Trees, KNN and Naïve Bayes algorithm to network features. Random Forest and decision tree algorithms displayed an accuracy of 99.1% and 98% respectively. All the methods, in combination predicted 71 common genes which were used to construct a gene regulatory network. The network was expanded to include 30 miRNAs targeting the genes. Interestingly, 39 out of 71 genes were transcription factors out of which ELN, SOX6 and FOXO3 genes are novel candidates in TOF. The work also provides a sub-module of genes and miRNAs supported by statistical models as prospective candidates to be biomarkers.

Received: 18 August 2023 | Revised: 27 September 2023 | Accepted: 8 October 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Correlation Analysis of DNA UGT1A1 Gene Expression in Hyperbilirubinemia and Central Nervous System-Related Adverse Reactions Following Oral Dolutegravir Administration

Samuel J. Bunu — 2023-10-30

Major pharmaceutical guidelines have identified Dolutegravir (DTG) as a critical medicine in the first-line treatment of HIV/AIDS. The goal of the study was to correlate DNA UGT1A1 gene expression in hyperbilirubinemia and central nervous system-related adverse reactions following oral Dolutegravir administration, using a NanoDrop-1000 spectrophotometer and rt-PCR. 52 seropositive patients were recruited using a standardized questionnaire having different components, including gender, demographic data, educational qualifications, when Highly Active-Antiretroviral Therapy (HAART) was initiated, the nature of adverse or side effects experienced, modalities used to overcome such events, and other laboratory parameters were carefully collected. Informed consent was obtained from all potential participants after an adequate explanation of the study and possible areas they would be involved, as well as samples to be collected. Blood samples were collected via venous puncture from all participants, and the same was used to quantify the level of UGT1A1 expressed in each participant using the NanoDrop-1000 spectrophotometer and rt-PCR. The outcome was compared with the side effects or adverse events experienced by each participant using ANOVA. The NanoDrop-1000 spectrophotometric results showed adequate DNA yield from 0.65 (lowest) to 104.71 ng/µl (highest) at 260nm and 280nm, respectively. At 260nm, the absorbance ranges from 0.01 to 2.09, while at 280nm from -0.01 to 1.12, respectively. Some of these participants reported having some level of anxiety, consistent headache, insomnia, drowsiness, dizziness, etc., which are clear neuropsychiatric symptoms, loss of appetite, dry throat, and anemia as adverse reactions due to HAART. There was a positive but mildly significant correlation between UGT1A1 gene expression and CNS-related adverse reactions and hyperbilirubinemia, with a p-value of 0.08.

Received: 1 August 2023 | Revised: 25 September 2023 | Accepted: 23 October 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review

Hao Lin — 2024-02-20

In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.

Received: 1 December 2023 | Revised: 31 January 2024 | Accepted: 18 February 2024

Conflicts of Interest

Hao Lin is the Editor-in-Chief for Medinformatics, and was not involved in the editorial review or the decision to publish this article. The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Inaugural Editorial: Editorial about the Founding of Medinformatics

Hao Lin — 2024-02-26

The convergence of biology and artificial intelligence represents a paradigm shift in scientific exploration. The integration of AI into the study of omics—genomics, proteomics, transcriptomics, metabolomics, and beyond—has revolutionized our ability to decipher the complexities of biological systems. From predictive modeling of gene structures to the elucidation of protein interactions, from the analysis of transcriptomic profiles to the exploration of epigenetic landscapes, AI has become an indispensable tool in the biologist's toolkit. In this journal that focuses on the integration of information and life medicine, we embark on countless journeys of artificial intelligence applications in biomedical fields, focusing on the application of omics analysis in analyzing biological processes or disease occurrence and development. In addition, this journal also focuses on the forefront of interdisciplinary research, with a particular focus on the knowledge and methods of artificial intelligence, mathematics, physics, and other disciplines to solve biomedical problems, enhancing our rational understanding of life and disease. As we founded this magazine, we invited researchers, scholars, and practitioners from different backgrounds to publish cutting-edge biomedical informatics research or join our editorial board. Let us explore the frontiers of knowledge, push the boundaries of innovation, and together, forge a brighter future for biomedical science.