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Recent advances in microarray and high throughput sequence analyses show that 87.3% of the human genome is actively transcribed, despite only 3% coding for proteins. This surprising fact suggests that non-coding RNA dominates the transcriptome. High-resolution microarray and massively parallel sequencing data showed that lncRNAs are nonprotein-coding transcripts. LncRNAs are polyadenylated ncRNAs longer than 200 nucleotides. Many lncRNAs are aberrantly expressed in many cancers, making them essential to tumour biology.  They play a significant role in the development, spread, and treatment of cancer at multiple stages of the disease's pathogenesis. Even though lncRNAs' functions are poorly understood, there is increasing evidence that they play critical roles in living organisms. To the contrary, it is now widely accepted that their dysregulation plays a role in the initiation and progression of a wide range of human cancers., especially in oral squamous cell carcinoma (OSCC). Consequently, it has the potential to be used as a molecular biomarker in the diagnosis and prognosis of certain forms of cancer. Long non-coding RNAs (lncRNAs) have recently been identified and characterized, revealing their regulatory significance OSCC. We comprehensively reviewed the role of lncRNAs in OSCC biology, highlighting their potential as biomarkers and diagnostic/therapeutic targets.

Next-generation sequencing (NGS) is a non-sanger technique based on high-throughput capacity for determining the exact order of nucleotide in the given DNA & RNA molecule. Modern high-throughput sequencer’s produce millions of sequences read in a single run. The quality of these data are not perfect because they contains artefacts like poor quality reads, adapter contamination, wrong base calls and INDELs due to instrument failure or chemical process error. In this study, a procedure has been designed for the refinement and improvement of quality of NGS data for further biological processes.

Asthma is a lung disease and incurable in nature. It can be only controlled by the proper medication after the correct diagnosis. In the absence of the correct diagnosis, it is very difficult to detect the different stages of asthma. In this study, for the riddance of misdiagnosis problem, expertise, knowledge has been translated into the machine learning and designed a tool for the diagnosis of Asthma. The experts’ knowledge and assessments on these four symptoms have been translated as input into Support vector machine (SVM) based on machine learning approaches. And the severity of Asthma has been diagnosed as an output. This tool has been developed in Microsoft Visual Studio 2010 with four input parameters Forced vital capacity (FVC), Forced Expiratory Volume in the first, second, Forced expiratory volume (FEV) (FEV1) (viz. FVC, and PERF) and one output in the form of severity. The efficacy of this tool is approximately 78.11%. This tool possibly will be useful for newer practitioners as well as researchers working in this area. The underlying technology for this tool is machine learning approach based on Support Vector Machine.