Methods in molecular biology
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The number of studies published in the biomedical literature has dramatically increased over the last few decades. This massive proliferation of literature makes clinical medicine increasingly complex, and information from multiple studies is often needed to inform a particular clinical decision. However, available studies often vary in their design, methodological quality, and population studied, and may define the research question of interest quite differently. ⋯ In addition, since even highly cited trials may be challenged over time, clinical decision-making requires ongoing reconciliation of studies which provide different answers to the same question. Because it is often impractical for readers to track down and review all the primary studies, systematic reviews and meta-analyses are an important source of evidence on the diagnosis, prognosis and treatment of any given disease. This chapter summarizes methods for conducting and reading systematic reviews and meta-analyses, as well as describes potential advantages and disadvantages of these publications.
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With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. ⋯ Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.
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Coronaviruses (CoVs), enveloped positive-sense RNA viruses, are characterized by club-like spikes that project from their surface, an unusually large RNA genome, and a unique replication strategy. CoVs cause a variety of diseases in mammals and birds ranging from enteritis in cows and pigs, and upper respiratory tract and kidney disease in chickens to lethal human respiratory infections. Most recently, the novel coronavirus, SARS-CoV-2, which was first identified in Wuhan, China in December 2019, is the cause of a catastrophic pandemic, COVID-19, with more than 8 million infections diagnosed worldwide by mid-June 2020. Here we provide a brief introduction to CoVs discussing their replication, pathogenicity, and current prevention and treatment strategies. We will also discuss the outbreaks of the highly pathogenic Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle Eastern Respiratory Syndrome Coronavirus (MERS-CoV), which are relevant for understanding COVID-19.
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A critical stage in performing gene editing experiments using the CRISPR/Cas9 system is the design of guide RNA (gRNA). In this chapter, we conduct a review of the current gRNA design rules for maximizing on-target Cas9 activity while minimizing off-target activity. In addition, we present some of the currently available computational tools for gRNA activity prediction and assay design.
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Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. ⋯ These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.