Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions.

Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer's disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer's disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer's disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer's disease.

Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers.

Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder's underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided.

Computational Models for Biomarker Discovery.

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and accurate prediction of disease progression are critical for developing effective therapeutic interventions. In recent years, computational models have emerged as powerful tools for biomarker discovery and disease prediction in Alzheimer's and other neurodegenerative diseases. This paper explores the use of computational models, particularly machine learning techniques, in analyzing large volumes of data and identifying patterns related to disease progression. The significance of early diagnosis, the challenges in classifying patients at the mild cognitive impairment (MCI) stage, and the potential of computational models to improve diagnostic accuracy are examined. Furthermore, the importance of incorporating diverse biomarkers, including genetic, molecular, and neuroimaging indicators, to enhance the predictive capabilities of these models is highlighted. The paper also presents case studies on the application of computational models in simulating disease progression, analyzing neurodegenerative cascades, and predicting the future development of Alzheimer's. Overall, computational models for biomarker discovery offer promising opportunities to advance our understanding of Alzheimer's disease, facilitate early diagnosis, and guide the development of targeted therapeutic strategies.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Exploring gene-drug interactions for personalized treatment of post-traumatic stress disorder.

Introduction: Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD. Methods: Through three methodological approaches, 122 genes and 184 Single Nucleotide Polymorphisms (SNPs) associated with PTSD were compiled into a single gene repository for PTSD. Using PharmGKB and DrugTargetor, 323 drug candidates were identified to target these 122 genes. The top 17 drug candidates were selected based on the statistical significance of the genetic associations, and their promiscuity (number of associated genestargets) and were further assessed for their suitability in terms of bioavailability and drug-like characteristics. Through functional analysis, insights were gained into the biological processes, cellular components, and molecular functions involved in PTSD. This formed the foundation for the next aspect of this study which was to propose an efficient treatment for PTSD by exploring drug repurposing methods. Results: The main aim was to identify the drugs with the most favorable profile that can be used as a pharmacological approach for PTSD treatment. More in particular, according to the genetic variations present in each individual, the relevant biological pathway can be identified, and the drug candidate proposed will specifically target said pathway, accounting for the personalized aspect of this work. The results showed that the drugs used as off-label treatment for PTSD have favorable pharmacokinetic profiles and the potential drug candidates that arose from DrugTargetor were not very promising. Clozapine showed a promising pharmacokinetic profile and has been linked with decreased psychiatric symptoms. Ambrucin also showed a promising pharmacokinetic profile but has been mostly linked with cancer treatment.

Extracellular vesicles and Alzheimer's disease in the novel era of Precision Medicine: implications for disease progression, diagnosis and treatment.

Extracellular vesicles (EVs), secreted membranous nano-sized particles, are critical intercellular messengers participating in nervous system homeostasis, while recent evidence implicates EVs in Alzheimer's disease (AD) pathogenesis. Specifically, small EVs have been shown to spread toxic proteins, induce neuronal loss, and contribute to neuroinflammation and AD progression. On the other hand, EVs can reduce amyloid-beta deposition and transfer neuroprotective substances between cells, mitigating disease mechanisms. In addition to their roles in AD pathogenesis, EVs also exhibit great potential for the diagnosis and treatment of other brain disorders, representing an advantageous tool for Precision Medicine. Herein, we summarize the contribution of small EVs to AD-related mechanisms and disease progression, as well as their potential as diagnostic and therapeutic agents for AD.

Genetic Pathways Involved in the Pathogenesis of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease. PD pathogenesis includes both genetic and environmental factors. Previous studies have linked the disease with several genes such as Parkin, SNCA, PINK1 and HTRA2. BiNGO software utilizes GO annotations in order to detect over-represented genes in terms of biological processes, cellular components and molecular functions. Three databases were utilized for this study (Ensembl, DisGeNET and UniProt). Data processing provided 110 genes associated with PD for further analysis. The aim of this study was to identify genes associated with PD and perform a functional enrichment analysis. Cytoscape and BiNGO software analysis presented several new genes that could play a potential role in pathogenesis of the disease. Future steps include additional research in order to establish the exact mechanism of action of these genes and pathways on PD.

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.

Computational Approaches Applied in the Field of Neuroscience.

Dementia describes a group of symptoms linked with cognitive decline. Alzheimer's disease (AD) is the most common form of dementia. Identifying accurate diagnostic biomarkers is a key goal. Technological advancements result in the generation of an ever-increasing volume of data. An interdisciplinary field of bioinformatics, known as machine learning (ML), allows scientists to explore and analyse said data. ML is broadly categorized into two groups: (i) unsupervised learning and (ii) supervised learning. This paper focuses on supervised learning methodologies. These approaches are not only helpful for biomarker discovery but for neuroimaging studies as well since they are able to analyse many variables simultaneously and to identify patterns in neuroimaging data. Furthermore, this paper lists several other computational approaches used for dementia care.

The Use of Translational Research Platforms in Clinical and Biomedical Data Exploration.

The rise of precision medicine combined with the variety of biomedical data sources and their heterogeneous nature make the integration and exploration of information that they retain more complicated. In light of these issues, translational research platforms were developed as a promising solution. Research centers have used translational tools for the study of integrated data for hypothesis development and validation, cohort discovery and data-exploration. For this article, we reviewed the literature in order to determine the use of translational research platforms in precision medicine. These tools are used to support scientists in various domains regarding precision medicine research. We identified eight platforms: BRISK, iCOD, iDASH, tranSMART, the recently developed OncDRS, as well as caTRIP, cBio Cancer Portal and G-DOC. The last four platforms explore multidimensional data specifically for cancer research. We focused on tranSMART, for it is the most broadly used platform, since its development in 2012.