High-throughput Big Data Research Articles

Artificial intelligence in healthcare: Setting new algo RHYTHM in medicine

Artificial intelligence (AI) is a rapidly expanding avenue in science and is often used to describe the use of computers, big data mining, modeling, and network that may simulate intelligent behavior and critical thinking similar to a human being but at an exceedingly fast pace. What lies at the heart of AI are intelligent algorithms that drive the whole process integratively, imparting the decisive ability to machines. The artificial intelligence market is expected to grow at a rate of 39.4% by 2022-2028. British mathematician Alan Turing’s 1950 paper “Computing Machinery and Intelligence " established AI's fundamental goal and vision. Fundamentally, AI is a branch of computer science that aims to answer Turing’s question of replicating human intelligence as machines. Since AI research aims to make smarter algorithms evolve and emulate human-like functioning, the degree to which an AI system can replicate human capabilities is used to determine the types of AI. The volume, magnitude, and complexity of data in healthcare mean that artificial intelligence (AI) will be increasingly applied and exploited, with several types of AI already being employed by researchers, clinicians, and data analysts. AI is a collective term, not just one technology, with an immediate relevance to healthcare. Few AI technologies of high importance to healthcare are: Machine learning, Neural Networks, and Deep Learning Natural Language Processing ( NLP) Physical Robots Rule-based expert systems The AI-associated healthcare market is growing at an exponential pace and is expected to reach USD 6.6 billion by 2021. Precision medicine initiatives can be divided into three types of clinical areas: complex algorithms, digital health applications, and “omics”-based tests. Large genomic datasets and informatics related to demographic data or diagnostic reports are exploited by machine learning algorithms for prediction and prognosis. This is coupled with patient healthcare data, such as treatment protocols, treatment response, response to therapy, and health monitoring, with data input from clinicians, researchers, and the public domain. High throughput big data from population-based studies are used with machine learning algorithms to establish correlations, build predictive models, and provide customized treatment protocols. In Omics based era, data from genomics, proteomics, and metabolomics are exceedingly used to address and identify health-related problems in large study cohorts . This will provide opportunities for translation and transition from bench to bedside through precision medicine.

LncRNA NKILA Promotes Cardiomyocytes Apoptosis by Targeting miR22-3p-TXNIP Signal Axis to Inhibit Proliferation, Migration, and Invasion of Cardiomyocytes under High Glucose-Induced Condition

This study aims to investigate the molecular mechanism of LncRNA NKILA underlying promoting cardiomyocytes apoptosis and relevant diabetic cardiomyopathy. We utilized high concentration of glucose to induce human cardiomyocytes cell line AC16 to imitate diabetic cardiomyopathy. And then, we performed high-throughput big data analysis, RT-PCR, and western blot assays to evaluate the expression levels of associated mRNA and protein. Cell apoptosis was tested by Annexin V-FITC. The proliferation, migration, and invasion of AC16 cells were examined by CCK8 assay, colony formation assay, EdU assay, wound healing test, and transwell chamber assay. We utilized statistical analysis and luciferase activity assay to analyze the interaction of relevant genes. LncRNA NKILA was highly expressed in AC16 cells induced by high glucose and inhibited AC16 cell proliferation, migration, and invasion by inducing cell apoptosis. Luciferase activity assay demonstrated that LncRNA NKILA binds to miR22-3p. The influence of LncRNA NKILA on AC16 cell proliferation, migration, and invasion could be reversed by miR22-3p. Luciferase activity assay demonstrated that TXNIP was a target of miR-22-3p in AC16 cells, and all the effects of TXNIP on AC16 cell proliferation, migration, and invasion could be abolished by miR22-3p. These results provided comprehensive data about a novel molecular mechanism of LncRNA NKILA promoting cardiomyocytes apoptosis: LncRNA NKILA performed its function in AC16 cells under high glucose-induced condition by targeting mir-22-3p-TXNIP signal axis, which indicated that LncRNA NKILA may play a crucial role in diabetic cardiomyopathy.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.