Advancing agricultural research using machine learning algorithms.
Ontology highlight
ABSTRACT:
SUBMITTER: Mourtzinis S
PROVIDER: S-EPMC8429560 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.
| S-EPMC7082403 | biostudies-literature
Project description:Post-operative recurrence in mycetoma after adequate medical and surgical treatment is common and a serious problem. It has health, socio-economic and psychological detrimental effects on patients and families. It is with this in mind, we set out to determine the predictors of post-operative recurrence in mycetoma. The study included 1013 patients with Madurella mycetomatis causing eumycetoma who underwent surgical excision at the Mycetoma Research Centre, Khartoum, Sudan in the period 1991-2015. The clinical records of these patients were reviewed and relevant information was collected using a pre-designed data collection sheet. The study showed, 276 patients (27.2%) of the studied population developed post-operative recurrence, 217 were males (78.6%) and 59 were females (21.4%). Their age ranged between 5 to 70 years with a mean of 32 years. The disease duration at presentation ranged between 2 months and 17 years. The majority of the patients 118 (42.8%) had mycetoma of 1 year duration. In this study, students were the most affected; 105 (38%) followed by workers 70 (25.4%), then farmers 48(17.3%). The majority of the patients were from the Central Sudan 207 (75%), Western Sudan 53 (19.2%) while 11 patients (4%) were from the Northern part. Past history of surgical intervention performed elsewhere was reported in 196 patients (71.1%). Family history of mycetoma was reported in 50 patients (18.1%). The foot was the most affected site, 245 (88.7%), followed by the hand seen in 19 (6.8%) patients and 44 (4.5%) had different sites involvement. Most of the patients 258 (93.5%) had wide local surgical excisions while 18 had major amputation. The model predicted that the certain groups have a high risk of recurrence, and these include patients with disease duration greater than 10 years and extra-pedal mycetoma. Patients with disease duration between [5-10] years, with pedal mycetoma, who had previous surgery, with positive family history and underwent wide local surgical excision. Patients with disease duration [5-10] years, with pedal mycetoma, had previous surgery, with no family history but presented with a disease size (> 10 cm), were non- farmers and underwent wide local surgical excision. Other groups are patients with disease duration (≤5 years), with pedal mycetoma, age <59 years, living in the Western /Eastern / Southern regions of the Sudan and with positive family history and had wide local surgical excision. Also included patients with disease duration (≤5 years), with pedal mycetoma, aged <59 years, living in the northern or central region, with no family history but presented with a disease size >10 cm, working as farmers or students and underwent wide local surgical excision. In conclusion, these groups of patients need special care to reduce the incidence of post-operative recurrence with its morbidity and detrimental consequences. In depth studies for the other predisposing factors for post-operative recurrence such as genetic, immunological and environmental factors are needed.
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Project description:Background:Epilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy. Methods:By analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model. Results: Epilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3.The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup. Conclusion: We believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway,JAK-STAT signaling pathway), and finally localized their key cellular subgroups(T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup,and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.
2023-01-25 | GSE223385 | GEO
Project description:The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
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Project description:We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.
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Project description:There is a paucity of predictive models for uncontrolled diabetes mellitus. The present study applied different machine learning algorithms on multiple patient characteristics to predict uncontrolled diabetes. Patients with diabetes above the age of 18 from the All of Us Research Program were included. Random forest, extreme gradient boost, logistic regression, and weighted ensemble model algorithms were employed. Patients who had a record of uncontrolled diabetes based on the international classification of diseases code were identified as cases. A set of features including basic demographic, biomarkers and hematological indices were included in the model. The random forest model demonstrated high performance in predicting uncontrolled diabetes, yielding an accuracy of 0.80 (95% CI: 0.79-0.81) as compared to the extreme gradient boost 0.74 (95% CI: 0.73-0.75), the logistic regression 0.64 (95% CI: 0.63-0.65) and the weighted ensemble model 0.77 (95% CI: 0.76-0.79). The maximum area under the receiver characteristics curve value was 0.77 (random forest model), while the minimum value was 0.7 (logistic regression model). Potassium levels, body weight, aspartate aminotransferase, height, and heart rate were important predictors of uncontrolled diabetes. The random forest model demonstrated a high performance in predicting uncontrolled diabetes. Serum electrolytes and physical measurements were important features in predicting uncontrolled diabetes. Machine learning techniques may be used to predict uncontrolled diabetes by incorporating these clinical characteristics.
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Project description:Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.
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