Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Missing value estimation using clustering and deep learning within multiple imputation framework

Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. Arguably the most popular imputation algorithm is multiple imputation by chained equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE’s linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses of six tabular data sets with up to 80% missing values and three missing types (missing completely at random, missing at random, missing not at random) reveal that ensemble or deep learning within MICE is superior to the baseline MICE (b-MICE), both of which are consistently outperformed by CISCL. Results show that CISCL + b-MICE outperforms b-MICE for all percentages and types of missing values. In most experimental cases, our proposed DNN-based MICE and gradient boosting MICE plus CISCL (GB-MICE-CISCL) outperform seven state-of-the-art imputation algorithms. The classification accuracy of GB-MICE-imputed data is further improved by our proposed GB-MICE-CISCL imputation method across all percentages of missing values. Results also reveal a shortcoming of the MICE framework at high percentages of missing values (50%) and when the missing type is not random. This paper provides a generalized approach to identifying the best imputation model for a tabular data set based on the percentage and type of missing values

Imputation Techniques in Machine Learning – A Survey

Machine learning plays a pivotal role in data analysis and information extraction. However, one common challenge encountered in this process is dealing with missing values. Missing data can find its way into datasets for a variety of reasons. It can result from errors during data collection and management, intentional omissions, or even human errors. It's important to note that most machine learning models are not designed to handle missing values directly. Consequently, it becomes essential to perform data imputation before feeding the data into a machine learning model. Multiple techniques are available for imputing missing values, and the choice of technique should be made judiciously, considering various parameters. An inappropriate choice can disrupt the overall distribution of data values and subsequently impact the model's performance. In this paper, various imputation methods, including Mean, Median, K-nearest neighbors (KNN)-based imputation, Linear Regression, Miss Forest, and MICE are examined

A Review of Missing Data Handling Techniques for Machine Learning

Real-world data are commonly known to contain missing values, and consequently affect the performance of most machine learning algorithms adversely when employed on such datasets. Precisely, missing values are among the various challenges occurring in real-world data. Since the accuracy and efficiency of machine learning models depend on the quality of the data used, there is a need for data analysts and researchers working with data, to seek out some relevant techniques that can be used to handle these inescapable missing values. This paper reviews some state-of-art practices obtained in the literature for handling missing data problems for machine learning. It lists some evaluation metrics used in measuring the performance of these techniques. This study tries to put these techniques and evaluation metrics in clear terms, followed by some mathematical equations. Furthermore, some recommendations to consider when dealing with missing data handling techniques were provided

A qualitative assessment of machine learning support for detecting data completeness and accuracy issues to improve data analytics in big data for the healthcare industry

Tackling Data Quality issues as part of Big Data can be challenging. For data cleansing activities, manual methods are not efficient due to the potentially very large amount of data. This paper aims to qualitatively assess the possibilities for using machine learning in the process of detecting data incompleteness and inaccuracy, since these two data quality dimensions were found to be the most significant by a previous research study conducted by the authors. A review of existing literature concludes that there is no unique machine learning algorithm most suitable to deal with both incompleteness and inaccuracy of data. Various algorithms are selected from existing studies and applied against a representative big (healthcare) dataset. Following experiments, it was also discovered that the implementation of machine learning algorithms in this context encounters several challenges for Big Data quality activities. These challenges are related to the amount of data particular machine learning algorithms can scale to and also to certain data type restrictions imposed by some machine learning algorithms. The study concludes that 1) data imputation works better with linear regression models, 2) clustering models are more efficient to detect outliers but fully automated systems may not be realistic in this context. Therefore, a certain level of human judgement is still needed