MicroRNAs (miRNAs) are a member of the family of non-coding RNA molecules, and consist of small conserved sequences between 19-25 nucleotides in length that are responsible for regulating many cellular functions by affecting a wide range of messenger RNAs in a sequence specific manner. Fundamental biological processes like cell proliferation and growth, stress resistance, tumorigenesis, fat metabolism, and neural development have all been shown to be governed by miRNAs. miRNAs carry out the post-transcriptional silencing of gene expression via targeting the 30-untranslated region (UTR) of the complementary mRNA sequence. The dysregulation of the expression levels of various miRNAs is typical of tumor cells, and has been associated with tumor progression and poor prognosis. Many miRNAs are up-regulated in cancer, where they can silence tumor suppressor genes such as apoptosis and immune response associated genes. Therefore, it is possible to profile the expression levels of miRNAs as biomarkers, in order to diagnose cancer and noncancerous diseases. Moreover, cancer detection in the early stages is crucial in clinical situations. Characterization of miRNAs in serum, plasma, and other bodily fluids, and understanding their stability against RNase degradation, is important to assess their suitability as biomarkers and diagnostic tools. Exosomes play an important role in inter-cellular communications, and these nanosized particles have various functions in diverse physiological pathways, in normal as well as abnormal cells. Exosomes can carry diverse cargos such as mRNAs, miRNAs, and proteins that transfer information between donor and recipient cells. Furthermore, uptake of exosomes and their cargos may promote or suppress various molecular and cellular pathways, which alter the cellular behavior. Many reports have discussed the role of exosomes released from cancer cells on the progression of cancer at various stages. Exosomes and their cargos may affect the growth of the tumor, metastasis, drug resistance, immune system function, as well as angiogenesis. Therefore, exosomes have been explored as diagnostic biomarkers in many cancers. Moreover, exosomes can be used as biological vehicles to deliver different drugs and agents like doxorubicin (DOX), miRNAs, and siRNAs. The present book covers the role of exosomes and micro-RNAs in the pathogenesis and treatment of various diseases.

Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandemic such as COVID-19, reliable true labels may be nearly impossible to obtain early on due to lack of testing equipment or other factors. In that scenario, identifying even a small amount of truly negative data may be impossible due to the high false negative rate of available tests. In such problems, it is possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. We are left with a small set of positive labeled data and a large set of unknown and unlabeled data. Readers will explore this Positive and Unlabeled learning (PU learning) problem in depth. The book rigorously defines the PU learning problem, discusses several common assumptions that are frequently made about the problem and their implications, and considers how to evaluate solutions for this problem before describing several of the most popular algorithms to solve this problem. It explores several uses for PU learning including applications in biological/medical, business, security, and signal processing. This book also provides high-level summaries of several related learning problems such as one-class classification, anomaly detection, and noisy learning and their relation to PU learning.