Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Zhang, Yuhang; Hu, Yu; Zhang, Yuchao; Hu, Landian; Kong, Xiangyin

doi:10.1016/j.bbadis.2017.12.003

Cited by 16 publications

(9 citation statements)

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“…Similarly to [27], they use a distributed implementation based on MapReduce on Hadoop in order to reduce execution time. In the same scope, in [28] Zhang et al apply a novel computational strategy to identify gene expression signatures in three types of hematopoietic cells, where each cell type is represented by its gene expression profile. To achieve this goal, the expression features are analyzed by a combination of a Monte Carlo feature selection (MCFS) algorithm and an optimized SVM classifier method, resulting in a feature list of the relevant gene expression.…”

Section: Embedded Methodsmentioning

confidence: 99%

Feature selection methods and genomic big data: a systematic review

et al. 2019

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With the advance of computational techniques, the amount of genomic data has risen exponentially, with a rapid rate [1] making it hard to utilize such data in the medical field without appropriate pre-processing, which in turn leads to more complexity and veracity issues [2] eventually creating multiple complications such as storage, analysis, privacy and security. Therefore, genomic data may look easy to handle in terms of its volume, but it actually requires quite a complicated process due to the complexity, heterogeneity and hybridity of its features. This process is entitled knowledge discovery process [3]: • Data recording Includes the different challenges and tools regarding the capture and storage of data. • Data pre-processing Which includes all the operations of cleaning and appropriation of the captured data to the ready to analyze form in order to optimize the analysis step. • Data analysis The task of evaluating data using different algorithms following a logical reasoning to examine each component of the data provided, with the aim of dispensing insightful outcomes.

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Section: Embedded Methodsmentioning

confidence: 99%

Feature selection methods and genomic big data: a systematic review

et al. 2019

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“…For detailed description of MCFS, please refer to [66,68]. To date, MCFS is being applied to tackle different biological problems [69,70,71,72,73,74].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Pan

Lei

Kong

et al. 2019

IJMS

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Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

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“…The SMO implements John Platt's sequential minimal optimization algorithm for training a support vector classifier [23]. In NN, we used the automatic mode for selecting the number of nodes in the hidden layer, with a learning rate of 0.3 and a momentum of 0.2 [24].…”

Section: Algorithm Selectionmentioning

confidence: 99%

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Serviá

Montserrat

Badía

et al. 2020

BMC Med Res Methodol

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Background Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques. Methods We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC. Results In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values in accuracy, specificity, and AUC, but obtain lower values in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC. Conclusion Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

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Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Cited by 16 publications

References 100 publications

Feature selection methods and genomic big data: a systematic review

Feature selection methods and genomic big data: a systematic review

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

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