scholarly journals New insights in Ulcerative Colitis Associated Gut Microbiota in South American Population: Akkermansia and Collinsella, two distinctive genera found in Argentine subjects.

2020 ◽  
Author(s):  
Ayelen D Rosso ◽  
Pablo Aguilera ◽  
Sofia Quesada ◽  
Jimena Cerezo ◽  
Renata Spiazzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ulcerative Colitis ◽  
Gut Microbiota ◽  
Intestinal Inflammation ◽  
System Response ◽  
Learning Models ◽  
Biochemical Tests ◽  
South American Population ◽  
Susceptible Host ◽  
Machine Learning Models

Background Globally, ulcerative colitis (UC) is the most common form of intestinal inflammation, which is believed to be the result of a deregulated immune system response to commensal microbiota in a genetically susceptible host. Multicellular organisms rely heavily on their commensal symbiotic microbiota, whose composition is closely related to intrinsic local characteristics and regulated or modified by environmental factors. In the present study we aim to describe the unknown gut microbiota of patients with UC in comparison with healthy individuals in order to find novel biomarkers for UC in our region. Methods We evaluated 46 individuals, 26 healthy non-UC controls and 20 UC patients, from the metropolitan area of Buenos Aires (BA), Argentina. Clinical features, biochemical tests and anthropometric measurements were determined. Fecal samples were collected and DNA was extracted for microbiota analysis. The hypervariable regions V3-V4 of the bacterial 16SR gene were sequenced using a MiSeq platform and sequences were analyzed using the QIIME2 environment. In addition, we looked for differential functional pathways using PICRUSt and compared the performance of three machine learning models to discriminate the studied individuals, using taxa and functional annotations. Results All UC patients were under clinical treatment with 70% of individuals in remission. We found no significant differences in gut microbiota richness or evenness between UC patients and non-UC controls (alpha diversity). Remarcably, microbial compositional structure within groups (beta diversity) showed differences: At the phylum level, Verrucomicrobia was overrepresented in controls while Actinobacteria was distinctive of UC patients; At the genus level Bacteroides and Akkermancia were significantly more abundant among controls while Eubacterium and Collinsella in UC patients. In addition, our results showed that carbohydrates metabolism was preponderant in UC patients, not observing a distinctive biochemical pathway for the healthy non-UC controls. Finally, in order to define a robust classifying method in our population, we evaluated the capability of three machine learning models to classify individuals. Our results reinforced the idea of functional compensation in microbiome communities, as models that used KEGG orthologs annotations had better capabilities than taxonomy to distinguish UC patients. Conclusions Our study provides new knowledge on the differences and similarities of the gut microbiota of UC patients as compared to non-UC controls of our population. This allows not only the association of local changes in gut microbial diversity with the pathology process, but also the future development of personalized nutritional and pharmacological therapies through the use of omic strategies describing the metagenomic profiles of the Argentine population.

scholarly journals Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors

2021 ◽  
Author(s):  
Xin Wang ◽  
Yuqing Yang ◽  
Jianchu Li ◽  
Rui Jiang ◽  
Ting Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gut Microbiota ◽  
Large Scale ◽  
Disease Risk ◽  
Bacterial Overgrowth ◽  
Considerable Proportion ◽  
Learning Models ◽  
Case Control Studies ◽  
Physiological Variables ◽  
Machine Learning Models

ABSTRACTHuman lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between many case-control studies for detecting disease-associated microbe existed and it is likely due to the limited sample size and the population-wide bias in human lifestyle and physiological variables. To infer association between whole gut microbiota and diseases accurately, we propose to build machine learning models by including both human variables and gut microbiota based on the American Gut Project data, the largest known publicly available human gut bacterial microbiota dataset. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent association of gut microbiota with the disease will be confirmed. We found that gut microbes showed different association strengths with different diseases. Adding gut microbiota into human variables enhanced the association strengths with inflammatory bowel disease (IBD) and unhealthy status; showed no effect on association strengths with Diabetes and IBS; reduced the association strengths with small intestinal bacterial overgrowth, C. difficile infection, lactose intolerance, cardiovascular disease and mental disorders. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be spurious. We also proposed a list of microbes as biomarkers to classify IBD and unhealthy status, and validated them by reference to previously published research.IMPORTANCEwe reexamined the association between gut microbiota and multiple diseases via machine learning models on a large-scale dataset, and by considering the effect of human variables ignored by previous studies, truly independent microbiota-disease associations were estimated. We found gut microbiota is associated independently with IBD and overall health of human, but more evidence is needed to judge associations between microbiota and other diseases. Further functional investigations of our reported disease-related microbes will improve understanding of the molecular mechanism of human diseases.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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