Cognitive performance protects against Alzheimer's disease independently of educational attainment and intelligence

MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction

Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interactions, limitations persist. Most methods rely on handcrafted features, restricting their applicability. They predominantly extract information from individual drugs, neglecting the importance of interaction details between drug pairs. To address these issues, we propose MGDDI, a graph neural network-based model for predicting potential adverse drug interactions. Notably, we use a multiscale graph neural network (MGNN) to learn drug molecule representations, addressing substructure size variations and preventing gradient issues. For capturing interaction details between drug pairs, we integrate a substructure interaction learning module based on attention mechanisms. Our experimental results demonstrate MGDDI's superiority in predicting adverse drug interactions, offering a solution to current methodological limitations.

Reserve, resilience and maintenance of episodic memory and other cognitive functions in aging

We tested if cognitive and brain reserve and maintenance explain individual differences in episodic memory and other cognitive domains from late middle to early older adulthood. We used The Vietnam Era Twin Study of Aging data (n=1604 men) with episodic memory measured at mean ages of 56, 62 and 68 years, and magnetic resonance imaging data for a subsample of participants (n=321). Cognitive reserve -young adult general cognitive ability at a mean age of 20 years and, to a lesser degree, educational attainment- was positively related to episodic memory performance at each assessment, but not to memory change. We found no evidence for the associations of brain reserve or brain maintenance on memory change. Results were highly similar when looking at processing speed, executive function and verbal fluency. In conclusion, higher young adult cognitive reserve was related to better episodic memory in midlife and older adulthood, but it did not confer better cognitive maintenance with respect to memory. This supports the importance of early cognitive development in dementia prevention.

Trends in cause-specific mortality among persons with Alzheimer's disease in South Carolina: 2014 to 2019

Introduction

Inconsistencies of reports contributes to the underreporting of Alzheimer's disease (AD) on death certificates. Whether underreporting exists within South Carolina has not been studied.

Methods

We conducted a prospective, population-based study on a cohort of persons (N = 78,534) previously diagnosed with AD and died between 2014-2019. We linked vital records with the South Carolina Alzheimer's Disease and Related Dementias Registry to investigate their cause of death and survival rates. Descriptive analyses calculated frequencies of demographic and health-related characteristics. Turnbull's method estimated the survival probabilities for different subgroups of patients. Hazard ratios were computed from the Cox proportional hazards model, adjusting for the following confounding variables of age at diagnosis, education level, gender, and race.

Results

The top immediate cause of death was Alzheimer's disease among all racial groups, except for Native American/American Indian. More females (60.3%) were affected by AD compared to males (39.7%). There is a 25% probability of survival, beyond 5 years, after AD diagnosis. Black/African American AD patients have the smallest risk of all-cause mortality across all racial/ethnic groups (HR 0.87; 95% CI, 0.85-0.89). Individuals with lower education had a lower likelihood of mortality.

Conclusion

Although AD was not underreported in the state of South Carolina further research is needed to develop protocols around classification of deaths among those diagnosed with dementia and comorbidities, including cardiovascular disease, to ensure dementia is properly reported as we move to prevent and treat Alzheimer's disease by 2025 and beyond.

Three-dimensional morphology scoring of hepatocellular carcinoma stratifies prognosis and immune infiltration

BACKGROUND

The morphological attributes could serve as pivotal indicators precipitating early recurrence and dismal overall survival in hepatocellular carcinoma (HCC), and quantifying morphological features may better stratify the prognosis of HCC.

OBJECTIVE

To develop a radiomics approach based on 3D tumor morphology features for predicting the prognosis of HCC and identifying differentially expressed genes related to morphology to guide HCC treatment.

MATERIALS AND METHODS

Retrospective study of 357 HCC patients. Radiomic features were extracted from MRI tumor regions; 14 morphology-related features predicted early HCC recurrence and patient stratification via LASSO-Cox modeling. Overall survival (OS) and recurrence-free survival (RFS) were analyzed. RNA sequencing from the Cancer Imaging Archive (TCIA) examined drug sensitivity and stratified HCC using morphological immunity genes, validating recurrence and prognosis.

RESULTS

Patients were split into training (n = 225), test (n = 132), and 50 TCIA dataset cohorts. Two features (Maximum2DdiameterColumn, Sphericity) in Cox regression stratified patients into high/low-risk Morphological Radiological Score (Morph-RS) groups. Significant OS and RFS were seen across all sets. Differentially expressed genes focused on T cell receptor signaling; low-risk group had higher T cells (P = 0.039), B cells (P = 0.041), NK cells (P = 0.018). SN-38, GSK2126458 might treat high-risk morphology. Morphology-immune genes stratified HCC, showing significant RFS/OS differences.

CONCLUSION

Tumor Morph-RS effectively stratifies HCC patients' recurrence and prognosis. Limited immune infiltration seen in Morph-RS high-risk groups signifies the potential of employing tumor morphology as a potent visual biomarker for diagnosing and managing HCC.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Associations of environmental factors with neurodegeneration: An exposome-wide Mendelian randomization investigation

Neurodegenerative diseases (NDDs) remain a global health challenge. Previous studies have reported potential links between environmental factors and NDDs, however, findings remain controversial across studies and elusive to be interpreted as evidence of robust causal associations. In this study, we comprehensively explored the causal associations of the common environmental factors with major NDDs including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS), based on updated large-scale genome-wide association study data through two-sample Mendelian randomization (MR) approach. Our results indicated that, overall, 28 significant sets of exposure-outcome causal association evidence were detected, 12 of which were previously underestimated and newly identified, including average weekly beer plus cider intake, strenuous sports or other exercises, diastolic blood pressure, and body fat percentage with AD, alcohol intake frequency with PD, apolipoprotein B, systolic blood pressure, and forced expiratory volume in 1 s (FEV1) with ALS, and alcohol intake frequency, hip circumference, forced vital capacity, and FEV1 with MS. Moreover, the causal effects of several environmental factors on NDDs were found to overlap. From a triangulation perspective, our investigation provided insights into understanding the associations of environmental factors with NDDs, providing causality-oriented evidence to establish the risk profile of NDDs.

Genome-wide analysis reveals extensive genetic overlap between childhood phenotypes and later-life type 2 diabetes

Observational studies have indicated a potential influence of childhood phenotypes on the later development of type 2 diabetes (T2D). However, the underlying biological mechanisms remain unclear. In this study, we conducted a comprehensive genome-wide analysis to investigate the shared genetic architecture and genetic loci between nine childhood phenotypes (N = 4202-620,26) and later-life T2D (N = 80,154) using genetic correlation, mendelian randomization (MR), and conjunctional false discovery rate (conjFDR) statistical frameworks. Our findings demonstrated substantial genetic correlations and pleiotropic enrichment between childhood obesity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and later-life T2D. Childhood obesity exhibited a significant association with increased later-life T2D risk through 10 mediators, 6 of which were adulthood obesity-related phenotypes. Additionally, we identified 69, 83, 3, 5, 10, 5, 3, and 7 loci shared between childhood obesity, BMI, SBP, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and T2D at conjFDR <0.05, with the majority of these loci being novel discoveries. Overall, our study reveals extensive genetic overlap between childhood obesity-related phenotypes and T2D with concordant effect directions, shedding new light on variants and phenotypes with lifelong effects.

Intersectional Effects of Race and Educational Attainment on Memory Function of Middle-Aged and Older Adults With Alzheimer's Disease

BACKGROUND

High educational attainment may protect individuals, particularly middle-aged and older adults, against a wide range of health risks, including memory decline with age; however, this protection is less clear in patients with Alzheimer's disease (AD). In addition, this effect may differ across racial groups. According to the Marginalized-Related Diminished Return (MDR) theory, for example, the protective effect of high educational attainment on mental and physical health shows a weaker protective effect for racial minority groups, particularly Black people compared to White individuals.

OBJECTIVES

This longitudinal study used data of middle-aged and older adults with AD with two aims: first, to test the association between educational attainment and memory, and second, to explore racial differences in this association in the USA.

METHODS

Data came from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The total sample was 1673 American middle-aged and older adults. The independent variable was educational attainment measured as years of education. The main outcome was memory operationalized as Rey Auditory Verbal Learning Test (RAVLT) Verbal Forgetting percentage (VF%). Age, gender, and follow-up duration were covariates. Race was the effect modifier. Linear regression model was utilized to analyze the data.

RESULTS

Of all participants, 68 (4.1%) were Black, and the remaining were White, with a mean age of 75 years old. In the pooled sample, educational attainment did not show a significant association with memory, independent of confounders. Educational attainment showed a significant interaction with race on memory, with higher educational attainment having a different effect on memory in White patients compared to Black patients.

CONCLUSION

The effect of higher educational attainment on memory differs for Black patients with AD compared to White patients. To prevent cognitive disparities by race, we need to go beyond racial inequality in access to resources (e.g., education) and minimize diminished returns of educational attainment for racial minorities. To tackle health inequalities, social policies should not be limited to equalizing socioeconomic status but also help minority groups leverage their available resources, such as educational attainment, and secure tangible outcomes.

Vitamin D, Cognition, and Alzheimer's Disease: Observational and Two-Sample Mendelian Randomization Studies

Background

Observational studies have found that vitamin D supplementation is associated with improved cognition. Further, recent Mendelian randomization (MR) studies have shown that higher vitamin D levels, 25(OH)D, may protect against Alzheimer's disease. Thus, it is possible that 25(OH)D may protect against Alzheimer's disease by improving cognition.

Objective

We assessed this hypothesis, by examining the relationship between 25(OH)D levels and seven cognitive measurements.

Methods

To mitigate bias from confounding, we performed two-sample MR analyses. We used instruments from three publications: Manousaki et al. (2020), Sutherland et al. (2022), and the Emerging Risk Factors Collaboration/EPIC-CVD/Vitamin D Studies Collaboration (2021).

Results

Our observational studies suggested a protective association between 25(OH)D levels and cognitive measures. An increase in the natural log of 25(OH)D by 1 SD was associated with a higher PACC score (BetaPACC score = 0.06, 95% CI = (0.04-0.08); p = 1.8×10-10). However, in the MR analyses, the estimated effect of 25(OH)D on cognitive measures was null. Specifically, per 1 SD increase in genetically estimated natural log of 25(OH)D, the PACC scores remained unchanged in the overall population, (BetaPACC score = -0.01, 95% CI (-0.06 to 0.03); p = 0.53), and amongst individuals aged over 60 (BetaPACC score = 0.03, 95% CI (-0.028 to 0.08); p = 0.35).

Conclusions

In conclusion, our MR study found no clear evidence to support a protective role of increased 25(OH)D concentrations on cognitive performance in European ancestry individuals. However, our study cannot entirely dismiss the potential beneficial effect on PACC for individuals over the age of 60.

Mendelian randomization study supports the causal effects of air pollution on longevity via multiple age-related diseases

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may reduce life expectancy; however, the causal pathways of PM2.5 exposure affecting life expectancy remain unknown. Here, we assess the causal effects of genetically predicted PM2.5 concentration on common chronic diseases and longevity using a Mendelian randomization (MR) statistical framework based on large-scale genome-wide association studies (GWAS) (>400,000 participants). After adjusting for other types of air pollution and smoking, we find significant causal relationships between PM2.5 concentration and angina pectoris, hypercholesterolaemia and hypothyroidism, but no causal relationship with longevity. Mediation analysis shows that although the association between PM2.5 concentration and longevity is not significant, PM2.5 exposure indirectly affects longevity via diastolic blood pressure (DBP), hypertension, angina pectoris, hypercholesterolaemia and Alzheimer's disease, with a mediated proportion of 31.5, 70.9, 2.5, 100, and 24.7%, respectively. Our findings indicate that public health policies to control air pollution may help improve life expectancy.

Large-scale genome-wide association study to identify causal relationships and potential mediators between education and autoimmune diseases

Objectives

Epidemiological studies suggested a potential connection between education and autoimmune disorders. This study investigated the possible cause-and-effect relationship using a Mendelian randomization approach.

Methods

We explored the causality between four education traits (n = 257,841~1,131,881) and 22 autoimmune diseases. The mediating role of smoking (632,802 individuals), BMI (681,275 individuals), alcohol (335,394 individuals), and income (397,751 individuals) was also investigated. Transcriptome-wide association study (TWAS) and enriched signaling pathways analysis were used to investigate the underlying biological mechanisms.

Results

Especially, higher cognitive performance was protective for psoriasis (odds ratio (OR) = 0.69, 95% confidence interval (CI) = 0.60-0.79, p = 6.12×10-8), rheumatoid arthritis (RA) (OR = 0.75, 95% CI = 0.67-0.83, p = 4.62×10-6), and hypothyroidism (OR = 0.83, 95% CI = 0.77-0.90, p = 9.82×10-6). Higher levels of educational attainment decreased risks of psoriasis (OR = 0.61, 95% CI = 0.52-0.72, p = 1.12×10-9), RA (OR = 0.68, 95% CI = 0.59-0.79, p = 1.56×10-7), and hypothyroidism (OR = 0.80, 95% CI = 0.72-0.88, p = 5.00×10-6). The completion of highest-level math class genetically downregulates the incidence of psoriasis (OR = 0.66, 95% CI = 0.58-0.76, p = 2.47×10-9), RA (OR = 0.71, 95% CI = 0.63-0.81, p = 5.28×10-8), and hypothyroidism (OR = 0.85, 95% CI = 0.79-0.92, p = 8.88×10-5). Higher self-reported math ability showed protective effects on Crohn's disease (CD) (OR = 0.67, 95% CI = 0.55-0.81, p = 4.96×10-5), RA (OR = 0.76, 95% CI = 0.67-0.87, p = 5.21×10-5), and psoriasis (OR = 0.76, 95% CI = 0.65-0.88, p = 4.08×10-4). Protein modification and localization, response to arsenic-containing substances may participate in the genetic association of cognitive performance on UC, RA, psoriasis, and hypothyroidism. According to mediation analyses, BMI, smoking, and income served as significant mediators in the causal connection between educational traits and autoimmune diseases.

Conclusion

Higher levels of education-related factors have a protective effect on the risk of several autoimmune disorders. Reducing smoking and BMI and promoting income equality can mitigate health risks associated with low education levels.

The Role of Insulin-like Growth Factor I in Mechanisms of Resilience and Vulnerability to Sporadic Alzheimer's Disease

Despite decades of intense research, disease-modifying therapeutic approaches for Alzheimer's disease (AD) are still very much needed. Apart from the extensively analyzed tau and amyloid pathological cascades, two promising avenues of research that may eventually identify new druggable targets for AD are based on a better understanding of the mechanisms of resilience and vulnerability to this condition. We argue that insulin-like growth factor I (IGF-I) activity in the brain provides a common substrate for the mechanisms of resilience and vulnerability to AD. We postulate that preserved brain IGF-I activity contributes to resilience to AD pathology as this growth factor intervenes in all the major pathological cascades considered to be involved in AD, including metabolic impairment, altered proteostasis, and inflammation, to name the three that are considered to be the most important ones. Conversely, disturbed IGF-I activity is found in many AD risk factors, such as old age, type 2 diabetes, imbalanced diet, sedentary life, sociality, stroke, stress, and low education, whereas the Apolipoprotein (Apo) E4 genotype and traumatic brain injury may also be influenced by brain IGF-I activity. Accordingly, IGF-I activity should be taken into consideration when analyzing these processes, while its preservation will predictably help prevent the progress of AD pathology. Thus, we need to define IGF-I activity in all these conditions and develop a means to preserve it. However, defining brain IGF-I activity cannot be solely based on humoral or tissue levels of this neurotrophic factor, and new functionally based assessments need to be developed.

Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer's disease

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer's disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = -0.22, p = 5.07E-05), cognitive performance (rg = -0.27, p = 2.44E-05), and intelligence (rg = -0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58-0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.

Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

Comparative evaluation and analysis of DNA N4-methylcytosine methylation sites using deep learning

DNA N4-methylcytosine (4mC) is significantly involved in biological processes, such as DNA expression, repair, and replication. Therefore, accurate prediction methods are urgently needed. Deep learning methods have transformed applications that previously require sequencing expertise into engineering challenges that do not require expertise to solve. Here, we compare a variety of state-of-the-art deep learning models on six benchmark datasets to evaluate their performance in 4mC methylation site detection. We visualize the statistical analysis of the datasets and the performance of different deep-learning models. We conclude that deep learning can greatly expand the potential of methylation site prediction.

Exploring the Effects of a Mediterranean Diet and Weight Loss on the Gut Microbiome and Cognitive Performance in Older, African American Obese Adults: A Post Hoc Analysis

African American adults have a higher prevalence of Alzheimer's dementia (AD) than non-Hispanic Whites. The impact of a Mediterranean Diet (Med Diet) and intentional weight loss (IWL) on the gut microbiome may alter AD risk. A post hoc analysis of the Building Research in Diet and Cognition (BRIDGE) trial was performed to determine whether participation in an 8-month Med Diet lifestyle intervention with (n = 35) or without IWL (n = 31) was associated with changes in gut microbiota structure, abundance, and function and whether these changes were related to changes in cognitive performance. The results showed that family and genus alpha diversity increased significantly in both groups combined (p = 0.0075 and p = 0.024, respectively). However, there were no other significant microbially related within- or between-group changes over time. Also, an increase in Med Diet adherence was significantly associated with a decrease in alpha diversity at the phylum level only (p = 0.049). Increasing alpha diversity was associated with decreasing cognitive performance, but this association was attenuated after controlling for Med Diet adherence. In sum, an 8-month Med Diet lifestyle intervention with or without IWL did not appreciably alter the gut microbiome.

A First Computational Frame for Recognizing Heparin-Binding Protein

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

Computational identification of promoters in Klebsiella aerogenes by using support vector machine

Promoters are the basic functional cis-elements to which RNA polymerase binds to initiate the process of gene transcription. Comprehensive understanding gene expression and regulation depends on the precise identification of promoters, as they are the most important component of gene expression. This study aimed to develop a machine learning-based model to predict promoters in Klebsiella aerogenes (K. aerogenes). In the prediction model, the promoter sequences in K. aerogenes genome were encoded by pseudo k-tuple nucleotide composition (PseKNC) and position-correlation scoring function (PCSF). Numerical features were obtained and then optimized using mRMR by combining with support vector machine (SVM) and 5-fold cross-validation (CV). Subsequently, these optimized features were inputted into SVM-based classifier to discriminate promoter sequences from non-promoter sequences in K. aerogenes. Results of 10-fold CV showed that the model could yield the overall accuracy of 96.0% and the area under the ROC curve (AUC) of 0.990. We hope that this model will provide help for the study of promoter and gene regulation in K. aerogenes.

Genetic correlation, causal relationship, and shared loci between vitamin D and COVID-19: A genome-wide cross-trait analysis

Observational studies have shown that vitamin D supplementation reduces the risk of COVID-19 infection, yet little is known about the shared genomic architectures between them. Leveraging large-scale genome-wide association study (GWAS) summary statistics, we investigated the genetic correlation and causal relationship between genetically determined vitamin D and COVID-19 using linkage disequilibrium score regression and Mendelian randomization (MR) analyses, and conducted a cross-trait GWAS meta-analysis to identify the overlapping susceptibility loci of them. We observed a significant genetic correlation between genetically predicted vitamin D and COVID-19 (r_g  = -0.143, p = 0.011), and the risk of COVID-19 infection would decrease by 6% for every 0.76 nmol L^-1 increase of serum 25 hydroxyvitamin D (25OHD) concentrations in generalized MR (OR = 0.94, 95% CI: 0.89-0.99, p = 0.019). We identified rs4971066 (EFNA1) as a risk locus for the joint phenotype of vitamin D and COVID-19. In conclusion, genetically determined vitamin D is associated with COVID-19. Increased levels of serum 25OHD concentration may benefit the prevention and treatment of COVID-19.

Deep ensemble learning for accurate retinal vessel segmentation

Significant progress has been made in deep learning-based retinal vessel segmentation in recent years. However, the current methods suffer from low performance and the robust of the models is not that good. Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. The results of benchmarking comparisons indicate that our model outperforms the existing ones on multiple datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field.

Empirical Comparison and Recent Advances of Computational Prediction of Hormone Binding Proteins Using Machine Learning Methods

Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.

KRAS is a prognostic biomarker associated with diagnosis and treatment in multiple cancers

KRAS encodes K-Ras proteins, which take part in the MAPK pathway. The expression level of KRAS is high in tumor patients. Our study compared KRAS expression levels between 33 kinds of tumor tissues. Additionally, we studied the association of KRAS expression levels with diagnostic and prognostic values, clinicopathological features, and tumor immunity. We established 22 immune-infiltrating cell expression datasets to calculate immune and stromal scores to evaluate the tumor microenvironment. KRAS genes, immune check-point genes and interacting genes were selected to construct the PPI network. We selected 79 immune checkpoint genes and interacting related genes to calculate the correlation. Based on the 33 tumor expression datasets, we conducted GSEA (genome set enrichment analysis) to show the KRAS and other co-expressed genes associated with cancers. KRAS may be a reliable prognostic biomarker in the diagnosis of cancer patients and has the potential to be included in cancer-targeted drugs.

MultiscaleDTA: a multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction

The task of predicting drug-target affinity (DTA) plays an increasingly important role at the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and achieved outstanding performance, which is beneficial for speeding up the development of new drugs. However, most convolutional neural networks (CNNs) based methods ignore the significance of information from CNN layers with different scales to DTA prediction. In addition, each feature provides different contributions to the final task. Therefore, in this study, we propose a novel end-to-end deep learning-based framework, called MultiscaleDTA, to predict drug-target binding affinity. MultiscaleDTA incorporates multi-scale CNNs and a self-attention mechanism to capture multi-scale and comprehensive features for characterizing the intrinsic properties of drugs and targets. Extensive experimental results on both regression and binary classification tasks demonstrate that MultiscaleDTA has achieved competitive performance compared to state-of-the-art methods.

Mendelian randomization analysis of the causal association of bone mineral density and fracture with multiple sclerosis

Multiple sclerosis (MS) is a neurodegenerative disorder and an autoimmune disease. Until now, observational studies have indicated the association of bone mineral density (BMD) and fracture with the risk of MS. However, these studies indicated inconsistent findings. Until now, genome-wide association studies (GWAS) have been conducted in BMD, fracture, and MS, which provide large-scale datasets to investigate the causal association of BMD and fracture with the risk of MS using the Mendelian randomization (MR) study. Here, we performed an MR study to clarify the causal association between BMD/fracture and the risk of MS using large-scale publicly available GWAS datasets from BMD, fracture, and MS. We first evaluated the bidirectional causal effects of BMD and MS. The main analysis method inverse-variance weighted (IVW) showed no significant causal effect of BMD on the risk of MS (β = 0.058, and p = 1.98E-01), and MS on the risk of BMD (β = −0.001, and p = 7.83E-01). We then evaluated the bidirectional causal effects of fracture and MS. However, we only identified a significant causal effect of fracture on the risk of MS using IVW (β = −0.375, p = 0.002), but no significant causal effect of MS on the risk of the fracture using IVW (β = 0.011, p = 2.39E-01). Therefore, our main analysis method IVW only found a significant causal effect of fracture on MS using the threshold for the statistically significant association p < 0.05/4 = 0.0125. Meanwhile, multivariable MR analyses showed that the causal effect of fracture on MS was independent of smoking, drinking, and obesity, but dependent on BMD. In summary, our MR analysis demonstrates that genetically increased fracture may reduce the risk of MS. Our findings should be further verified and the underlying mechanisms should be further evaluated by future studies.