15,435 research outputs found
Android Malware Characterization using Metadata and Machine Learning Techniques
Android Malware has emerged as a consequence of the increasing popularity of
smartphones and tablets. While most previous work focuses on inherent
characteristics of Android apps to detect malware, this study analyses indirect
features and meta-data to identify patterns in malware applications. Our
experiments show that: (1) the permissions used by an application offer only
moderate performance results; (2) other features publicly available at Android
Markets are more relevant in detecting malware, such as the application
developer and certificate issuer, and (3) compact and efficient classifiers can
be constructed for the early detection of malware applications prior to code
inspection or sandboxing.Comment: 4 figures, 2 tables and 8 page
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
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