Rohit Gupta

Bio: Rohit Gupta is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Switched capacitor & Voltage source. The author has an hindex of 2, co-authored 3 publications receiving 29 citations. Previous affiliations of Rohit Gupta include Indraprastha Institute of Information Technology.

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Abstract: Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.

Resistance-to-digital converter designed for high power-line interference rejection capability

Abstract: A novel resistance-to-digital converter (RDC), based on the integrating type analogue-to-digital converter principle, is presented in this study. The conversion time of the proposed scheme is not a function of the current value of the parameter being measured. Thus, by suitably setting this parameter, the converter can be made to reject the effects of interference at a particular frequency, such as, that due to power-line at 50/60 Hz. Error analysis was conducted to ascertain the effects of non-idealities of various components of the circuit, on its output. Simulation studies were carried out in LTSPICE to verify the linearity and the interference rejection capability of the converter. Further, a prototype RDC was developed in the laboratory and tested to confirm the results of the simulation.

A modified RDC with an auto-adjustable SC source enabled auto-calibration scheme

Abstract: A novel Dual-Slope Resistance-to-Digital Converter (DSRDC) suitable for single element resistive sensors is presented in this paper. The digital output of the converter is directly proportional to the measurand and is insensitive to the nominal value of resistance, Ro, of the sensor. The choice of fixed resistance employed in a typical DSRDC has been made to be independent of Ro and the constraints on its accuracy have been relaxed. This makes the Resistance-to-Digital Converter versatile, in a manner that it can be easily interfaced with a wide range of resistive sensors having different values of Ro. This task was accomplished by employing an automatic calibration technique, wherein, the reference voltage is digitally adjusted by a Switched Capacitor Controlled Voltage Source (SCCVS). A prototype DSRDC was developed and tested against various values of Ro, which were in-turn emulated using known resistances of values ranging from 25 ku to 200 ku. The worst case error in reading was observed to be less than 0.65 %.

The somatic genomic landscape of glioblastoma

Abstract: We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.

Abstract: In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.

Abstract: While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.