iQuery and iCorrelation Engine (iCE) by iNDX.Ai are software solutions that combine the power of machine learning, artificial intelligence and natural language processing. They are designed for carrying out multifaceted analysis between multiomics as well as clinical data.
Fremont, CA: The highly user-friendly software applications by Indx.Ai, the iCorrelation Engine, and iQuery are developed with an aim to enhance scientific diagnosis in complex oncology processes including multiomics, in terms of both accuracy and speed.
Multiomics is an innovative approach towards scientific diagnosis of diseases, especially in oncology researches, where data sets of various omic groups are combined for the study. These omic groups include Genome, Proteomes, Metabolome, Transcriptome, Epigenome and Microbiome.
Multiomics is a critical aspect of cancer research. Any form of cancer tends to be heterogeneous in nature. Multiomics enables comprehensive insights into the disease and the patient. However, a traditional cancer research and diagnostic option is limited to assessing individual targets in isolation.
Data analysis is the most crucial challenge in Multiomics. There can be a number of targets to choose from while conducting the experiments. For example, an RNA-seq analysis may lead to several hits. It is possible that a number of these hits may turn irrelevant during the later stages of the experiment. While combining data received from other genomes in multiomics, newer developments may arise. However, when the researcher carries out a single database analysis, these developments may not appear. As a result, a correlative approach is difficult to implement.
Both iQuery and iCE are developed in accordance with these challenges and facilitates to conduct a cross-functional study on clinical data and omics. These applications encompass a user-friendly interface that doesn’t require high-end knowledge on advanced bioinformatics. Moreover, iQuery and iCE automate most of the features of analysing data. Through this approach, researchers can devote their valuable time on critical features of the research instead of spending hours on probing day-to-day statistical functions.