Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Dessie, Eskezeia Y.; Gautam, Yadu; Ding, Lili; Altaye, Mekibib; Beyene, Joseph; Mersha, Tesfaye B.

doi:10.1038/s41598-023-35866-2

Cited by 5 publications

(7 citation statements)

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“…A sign of the intricacy of AD categorization is the discovery of trade-offs across various measures (Accuracy, precision, recall, F1-score, and AUC ROC) used to evaluate the model performance. This is a common practice followed by a number of earlier studies that have emphasized the necessity to use multiple criteria to objectively evaluate classification models instead of relying on a single one [37], [38], [39].…”

Section: Discussionmentioning

confidence: 99%

AITeQ: A machine learning framework for Alzheimer’s prediction using a distinctive 5-gene signature

Ahammad,

Lamisa,

Bhattacharjee

et al. 2023

Preprint

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Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool using RNA-Seq (AITeQ), a machine learning model based on an optimized random forest algorithm for identification of Alzheimer's from RNA-Seq data. Analysis of RNA-Seq data from 433 individuals, including 293 Alzheimer's patients and 140 controls led to the discovery of 47,929 differentially expressed genes. This was followed by a machine learning protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of 4 different methodologies, culminated in the identification of a compact yet impactful set of 5 genes. Ten diverse machine learning models were trained and tested using these 5 genes (ITGA10, CXCR4, ADCYAP1, SLC6A12, VGF). Performance metrics, including precision, recall, F1-score, accuracy, receiver operating characteristic area under the curve, and confusion matrices, were assessed before and after hyperparameter tuning. Overall, the random forest model with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ

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Section: Discussionmentioning

confidence: 99%

AITeQ: A machine learning framework for Alzheimer’s prediction using a distinctive 5-gene signature

Ahammad,

Lamisa,

Bhattacharjee

et al. 2023

Preprint

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“…The training set was used to train the model and the test set was used to evaluate the performance of the model. Standard ML accuracy measures were used to evaluate the prediction power of popular supervised ML algorithms, including SVM (13), LR (11,14), 19), 20,21), and NB (19). The ML algorithms were trained based on 10-fold cross-validation to optimize models.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…The anthropometric measurements were taken referring to WHO (20) recommended guidelines for measuring weight and height (length) of under 2 years children. For height, the UNICEF height board was used to measure the length of the child and read to the nearest 0.1 cm.…”

Section: Anthropometric Measurementmentioning

confidence: 99%

“…(5) dark green vegetables; (6) vitamin A-rich vegetables; (7) other vegetables; (8) vitamin A-rich fruits; (9) other fruits; (10) red palm oil; (11) dairy; (12) meat and poultry; (13) organ meat (i.e., liver, heart, intestines or kidney); (14) eggs; (15) fish and seafood; (16) oils and fats; (17) savory and fried snacks; (18) sweets, confectionery and sweetened beverages; (19) condiments; (20) other beverages (e.g., tea, coffee and alcohol). A score of 1 (if consumed) or 0 (if not consumed) was assigned, to give a maximum score of 20.…”

Section: Dietary Diversity Indicatorsmentioning

confidence: 99%

“…The current Demographic Health Survey (DHS) aggregated statistics in Rwanda for under 5 years children is 33.1% for stunting levels and wasting at 1.1% (12) with the stunting levels for [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]21.3,41.6 and 49.4%,respectively. In Rwanda,only 22 % (12) children of age between 6 to 23 months adhere to the infant young child feeding practices in terms of minimum acceptable diet, despite the high stunting prevalence that is very high (stunting ≥30%) according WHO threshold (13).…”

Section: Introductionmentioning

confidence: 99%

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New Challenges and Future Perspectives in Nutrition and Sustainable Diets in Africa

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Enhancing selection of alcohol consumption-associated genes by random forest

Lyu,

Joehanes,

Huan

et al. 2024

Br J Nutr

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Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analyzed array-based, whole-blood derived expression data for 17,873 gene transcripts in 5,508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised Random Forest (RF)-based feature selection method, we selected 25 alcohol-associated transcripts. In a testing set (30% of entire study participants), AUCs (area under the receiver operating characteristics curve) of these 25 transcripts were 0.73, 0.69, and 0.66 for nondrinkers vs. moderate drinkers, nondrinkers vs. heavy drinkers, and moderate drinkers vs. heavy drinkers, respectively. The AUCs of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, e.g., AUCs of 1,985 transcripts identified by conventional linear regression models (false discovery rate < 0.05) were 0.72, 0.68, and 0.68, respectively. With Bonferroni correction for the 25 Boruta method selected transcripts and three CVD risk factors (i.e., at P < 6.7e-4), we observed 13 transcripts were associated with obesity, 3 transcripts with type 2 diabetes, and 1 transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.

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Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Cited by 5 publications

References 50 publications

AITeQ: A machine learning framework for Alzheimer’s prediction using a distinctive 5-gene signature

AITeQ: A machine learning framework for Alzheimer’s prediction using a distinctive 5-gene signature

New Challenges and Future Perspectives in Nutrition and Sustainable Diets in Africa

Enhancing selection of alcohol consumption-associated genes by random forest

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