LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction.
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ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC9316258 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSchizophrenia (SC) is one of the most common psychiatric diseases. Its potential pathogenic genes and effective treatment methods are still unclear. Cell senescence has been confirmed in mental diseases. A link exists between cellular senescence and immunity, and immune-related problems affect suicide rates in individuals suffering from schizophrenia. Therefore, the aims of this study were to identify candidate genes based on cell senescence that can affect the diagnosis and treatment of schizophrenia.MethodsTwo data sets of schizophrenia were provided by the Gene Expression Omnibus (GEO) database, one was taken as training and the other as a validation group. The genes related to cell senescence were obtained from the CellAge database. DEGs were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). The function enrichment analysis was conducted, followed by machine learning-based identification for least absolute shrinking and selection operators (LASSO) regression. Random Forest were used to identify candidate immune-related central genes and establish artificial neural networks for verification of the candidate genes. The receiver operating characteristic curve (ROC curve) was used for the diagnosis of schizophrenia. Immune cell infiltrates were constructed to study immune cell dysregulation in schizophrenia, and relevant drugs with candidate genes were collected from the DrugBank database.ResultsThirteen co-expression modules were screened for schizophrenia, of which 124 were the most relevant genes.There were 23 intersected genes of schizophrenia (including DEGs and the cellular senescence-related genes), and through machine learning six candidate genes were finally screened out. The diagnostic value was evaluated using the ROC curve data. Based on these results it was confirmed that these candidate genes have high diagnostic value.Two drugs related to candidate genes, Fostamatinib and Ritodine, were collected from the DrugBanks database.ConclusionSix potential candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, and ID1) had been identified, all of which had diagnostic significance. Fostamatinib might be a drug choice for patients with schizophrenia to develop immune thrombocytopenic purpura (ITP) after treatment, providing effective evidence for the pathogenesis and drug treatment of schizophrenia.
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Project description:Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime prediction are available in the literature. On the other hand, they suffer from a deficiency in predicting cyber-crime and cyber-attack methods. This problem can be tackled by identifying an attack and the perpetrator of such attack, using actual data. The data include the type of crime, gender of perpetrator, damage and methods of attack. The data can be acquired from the applications of the persons who were exposed to cyber-attacks to the forensic units. In this paper, we analyze cyber-crimes in two different models with machine-learning methods and predict the effect of the defined features on the detection of the cyber-attack method and the perpetrator. We used eight machine-learning methods in our approach and concluded that their accuracy ratios were close. The Support Vector Machine Linear was found out to be the most successful in the cyber-attack method, with an accuracy rate of 95.02%. In the first model, we could predict the types of attacks that the victims were likely to be exposed to with a high accuracy. The Logistic Regression was the leading method in detecting attackers with an accuracy rate of 65.42%. In the second model, we predicted whether the perpetrators could be identified by comparing their characteristics. Our results have revealed that the probability of cyber-attack decreases as the education and income level of victim increases. We believe that cyber-crime units will use the proposed model. It will also facilitate the detection of cyber-attacks and make the fight against these attacks easier and more effective.
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Project description:Leaf senescence is a tightly controlled and complex developmental process that shares many similarities across species, yet our understanding of the underlying conserved molecular mechanisms is still lacking. Here, we observed functional conservation of leaf senescence underlying pathways in A. thaliana, O. sativa, and S. lycopersicum. From machine learning-based integration of data from nearly 10 000 samples to obtain a universal regulatory network of leaf senescence, it was found that mitostasis is the cross-species central biological hub. We measure and compare changes in the transcriptome and metabolome of A. thaliana, O. sativa, and S. lycopersicum leaves under mitostress/natural senescence. In data from different species, mitostasis-related transcription factors binding site enrichment and amino acids expression changes converge on putative senescence modulators. Our study provides a cross-species, multi-omics perspective for understanding the leaf senescence conserved mechanisms.
2022-05-03 | GSE201607 | GEO
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status.
2013-01-01 | GSE29210 | GEO
Project description:Current methods in measuring maize (Zea mays L.) southern rust (Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92-98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.
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Project description:Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5-25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.
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Project description:BACKGROUND:As an essential component in reducing anthropogenic CO2 emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stabilize and reduce net zero global anthropogenic CO2 emissions. Tree planting was identified as an effective method to offset CO2 emissions. A high net photosynthetic rate (Pn) with fast-growing trees could efficiently fulfill the goal of CO2 emission reduction. Net photosynthetic rate model can provide refernece for plant's stability of photosynthesis productivity. METHODS AND RESULTS:Using leaf phenotype data to predict the Pn can help effectively guide tree planting policies to offset CO2 release into the atmosphere. Tree planting has been proposed as one climate change solution. One of the most popular trees to plant are poplars. This study used a Populus simonii (P. simonii) dataset collected from 23 artificial forests in northern China. The samples represent almost the entire geographic distribution of P. simonii. The geographic locations of these P. simonii trees cover most of the major provinces of northern China. The northwestern point reaches (36°30'N, 98°09'E). The northeastern point reaches (40°91'N, 115°83'E). The southwestern point reaches (32°31'N, 108°90'E). The southeastern point reaches (34°39'N, 113°74'E). The collected data on leaf phenotypic traits are sparse, noisy, and highly correlated. The photosynthetic rate data are nonnormal and skewed. Many machine learning algorithms can produce reasonably accurate predictions despite these data issues. Influential outliers are removed to allow an accurate and precise prediction, and cluster analysis is implemented as part of a data exploratory analysis to investigate further details in the dataset. We select four regression methods, extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF) and generalized additive model (GAM), which are suitable to use on the dataset given in this study. Cross-validation and regularization mechanisms are implemented in the XGBoost, SVM, RF, and GAM algorithms to ensure the validity of the outputs. CONCLUSIONS:The best-performing approach is XGBoost, which generates a net photosynthetic rate prediction that has a 0.77 correlation with the actual rates. Moreover, the root mean square error (RMSE) is 2.57, which is approximately 35 percent smaller than the standard deviation of 3.97. The other metrics, i.e., the MAE, R2, and the min-max accuracy are 1.12, 0.60, and 0.93, respectively. This study demonstrates the ability of machine learning models to use noisy leaf phenotype data to predict the net photosynthetic rate with significant accuracy. Most net photosynthetic rate prediction studies are conducted on herbaceous plants. The net photosynthetic rate prediction of P. simonii, a kind of woody plant, illustrates significant guidance for plant science or environmental science regarding the predictive relationship between leaf phenotypic characteristics and the Pn for woody plants in northern China.
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