Publications by authors named "G Stolovitzky"
Extracellular vesicles (EVs) are heterogeneous entities secreted by cells into their microenvironment and systemic circulation. Circulating EVs carry functional small RNAs and other molecular footprints from their cell of origin, and thus have evident applications in liquid biopsy, therapeutics, and intercellular communication. Yet, the complete transcriptomic landscape of EVs is poorly characterized due to critical limitations including variable protocols used for EV-RNA extraction, quality control, cDNA library preparation, sequencing technologies, and bioinformatic analyses.
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- All cells, regardless of being eukaryotic or prokaryotic, release extracellular vesicles (EVs) for various functions like communication and waste disposal, with small EVs containing small RNAs that may serve as important disease markers.
- This study focuses on identifying unannotated small RNAs in EVs from prostate cancer and benign tissues, overcoming limitations of previous sequencing methods to explore the 'dark matter' of genomes and their role in gene expression regulation.
- Researchers found that these novel EV-associated small RNAs, termed EV-UGRs, showed a significant reduction in aggressive prostate cancer, but their expression increased after treatment, potentially promising for fluid-based diagnostics in cancer screening.
Background: Clinical trials are vital for developing new therapies but can also delay drug development. Efficient trial data management, optimized trial protocol, and accurate patient identification are critical for reducing trial timelines. Natural language processing (NLP) has the potential to achieve these objectives.
View Article and Find Full Text PDFMotivation: The integration of vast, complex biological data with computational models offers profound insights and predictive accuracy. Yet, such models face challenges: poor generalization and limited labeled data.
Results: To overcome these difficulties in binary classification tasks, we developed the Method for Optimal Classification by Aggregation (MOCA) algorithm, which addresses the problem of generalization by virtue of being an ensemble learning method and can be used in problems with limited or no labeled data.
Article Synopsis
- Ground-glass opacities (GGOs) on CT scans may signal lung cancer, and leveraging electronic health records filled with unstructured notes can aid in managing these nodules effectively.
- Researchers developed an advanced deep learning natural language processing (NLP) tool to extract detailed GGO features from radiology notes of over 13,000 lung cancer patients, achieving high levels of precision and recall in their analysis.
- The longitudinal study of GGO status showed that about 16.8% of patients experienced increased size of GGOs, while 72.3% had stable conditions, indicating the tool's efficacy in monitoring and analyzing GGO progression over time.