Machine Learning: An Overview and Applications in Pharmacogenetics

Artificial Intelligence Applications in the Diagnosis of Neuromuscular Diseases: A Narrative Review

The accurate diagnosis of neuromuscular diseases (NMD) is in many cases difficult; the starting point is the clinical approach based on the course of the disease and a careful physical examination of the patient. Electrodiagnostic tests, imaging, muscle biopsy, and genetics are fundamental complementary studies for the diagnosis of NMD. The large volume of data obtained from such studies makes it necessary to look for efficient solutions, such as artificial intelligence (AI) applications, which can help classify, synthesize, and organize the information of patients with NMD to facilitate their accurate and timely diagnosis. The objective of this study was to describe the usefulness of AI applications in the diagnosis of patients with neuromuscular diseases. A narrative review was done, including publications on artificial intelligence applied to the diagnostic methods of NMD currently existing. Twelve studies were included. Two of the studies focused on muscle ultrasound, five of the studies on muscle MRI, two studies on electromyography, two studies on amyotrophic lateral sclerosis (ALS) biomarkers, and one study on genes related to myopathies. The accuracy of classification using different classification algorithms used in each of the studies included in this narrative review was already 90% in most studies. In conclusion, the future design of more accurate algorithms applied to NMD with greater precision will have an impact on the earlier diagnosis of this group of diseases.

Pharmacogenetic testing in psychiatry: Perspective on clinical utility

Pharmacogenetic studies the influence of inherited characteristics on medication. While different from pharmacogenomics, which is a study of the entire genome in relation to medication effect, their distinction remains inconsistent, and the two terms are used interchangeably. Although the potential of pharmacogenomics in psychiatry is apparent and its clinical utility is suboptimal, the uptake of recommendations and guidelines is minimal and research into PGx is not diverse. This article offers an overview of pharmacogenetics (PGx) in psychiatry, explores the difficulties, and provides recommendations on improving its applicability and clinical utility.

Supporting Machine Learning Model in the Treatment of Chronic Pain

Conventional therapy options for chronic pain are still insufficient and patients most frequently request alternative medical treatments, such as medical cannabis. Although clinical evidence supports the use of cannabis for pain, very little is known about the efficacy, dosage, administration methods, or side effects of widely used and accessible cannabis products. A possible solution could be given by pharmacogenetics, with the identification of several polymorphic genes that may play a role in the pharmacodynamics and pharmacokinetics of cannabis. Based on these findings, data from patients treated with cannabis and genotyped for several candidate polymorphic genes (single-nucleotide polymorphism: SNP) were collected, integrated, and analyzed through a machine learning (ML) model to demonstrate that the reduction in pain intensity is closely related to gene polymorphisms. Starting from the patient's data collected, the method supports the therapeutic process, avoiding ineffective results or the occurrence of side effects. Our findings suggest that ML prediction has the potential to positively influence clinical pharmacogenomics and facilitate the translation of a patient's genomic profile into useful therapeutic knowledge.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

Genetic Association Study and Machine Learning to Investigate Differences in Platelet Reactivity in Patients with Acute Ischemic Stroke Treated with Aspirin

Aspirin resistance (AR) is a pressing problem in current ischemic stroke care. Although the role of genetic variations is widely considered, the data still remain controversial. Our aim was to investigate the contribution of genetic features to laboratory AR measured through platelet aggregation with arachidonic acid (AA) and adenosine diphosphate (ADP) in ischemic stroke patients. A total of 461 patients were enrolled. Platelet aggregation was measured via light transmission aggregometry. Eighteen single-nucleotide polymorphisms (SNPs) in ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1 and PEAR1 genes and the intergenic 9p21.3 region were determined using low-density biochips. We found an association of rs1330344 in the PTGS1 gene with AR and AA-induced platelet aggregation. Rs4311994 in ADRA2A gene also affected AA-induced aggregation, and rs4523 in the TBXA2R gene and rs12041331 in the PEAR1 gene influenced ADP-induced aggregation. Furthermore, the effect of rs1062535 in the ITGA2 gene on NIHSS dynamics during 10 days of treatment was found. The best machine learning (ML) model for AR based on clinical and genetic factors was characterized by AUC = 0.665 and F1-score = 0.628. In conclusion, the association study showed that PTGS1, ADRA2A, TBXA2R and PEAR1 polymorphisms may affect laboratory AR. However, the ML model demonstrated the predominant influence of clinical features.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

A survey of physician opinions in Russia in the field of pharmacogenetics of cardiovascular disease

Aims: To study the readiness of Russian physicians with experience and younger physicians undergoing clinical residency and postgraduate education to apply pharmacogenetic testing in their clinical practice. Materials & methods: The sociological study involved physicians (n = 378) living in different regions of the Russian Federation, as well as residents and graduate students (n = 185) of the Russian Medical Academy of Continuing Professional Medical Education. The survey consisted of 35 questions, and 23 were created on the online platform of professional surveys, Testograf.ru. Results: Every second respondent was willing to use pharmacogenetic testing in clinical practice to predict the efficacy and safety of medications in patients with cardiovascular disease (p = 0.06). Factors impeding the clinical implementation of pharmacogenetic testing in Russia were identified: physicians' ignorance of pharmacogenetics (p = 0.015), a lack of pharmacogenetic testing in clinical guidelines and treatment standards (p = 0.175) and a lack of economic justification for using pharmacogenetic testing (p = 0.320). Conclusion: Russian physicians have a positive attitude toward pharmacogenetic testing. However, the level of test implementation remains low.

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

Pharmacogenomics: A Step forward Precision Medicine in Childhood Asthma

Personalized medicine, an approach to care in which individual characteristics are used for targeting interventions and maximizing health outcomes, is rapidly becoming a reality for many diseases. Childhood asthma is a heterogeneous disease and many children have uncontrolled symptoms. Therefore, an individualized approach is needed for improving asthma outcomes in children. The rapidly evolving fields of genomics and pharmacogenomics may provide a way to achieve asthma control and reduce future risks in children with asthma. In particular, pharmacogenomics can provide tools for identifying novel molecular mechanisms and biomarkers to guide treatment. Emergent high-throughput technologies, along with patient pheno-endotypization, will increase our knowledge of several molecular mechanisms involved in asthma pathophysiology and contribute to selecting and stratifying appropriate treatment for each patient.