Biology subjects

Ahmad, N.

Publications and source records attributed to Ahmad, N..

21 records · Page 2Linked to original sources

bioRxiv2018

Differential Expression Analysis of ZIKV Infected Human RNA Sequence Reveals Potential Biomarkers

Zika virus (ZIKV) is considered to be an emerging viral outbreak due to its link to diseases like microcephaly, Guillain-Barre Syndrome in human. In this paper, we identify differentially expressed genes (DEGs) using RNA-seq data. In this study, we adopted the RNA-seq analysis pipeline to quantify RNA-seq data into read counts. Our analysis uncovers the significant DEGs which may be involved in the altered biological process somehow. Here, we report the list of significant DEGs, out of which three genes are found to be highly differentially expressed. In addition, our analysis also predicts other moderate DEGs, low DEGs whose differential expression was induced due to ZIKV infections.

bioinformatics↗

bioRxiv2018

Spike: A GPU Optimised Spiking Neural Network Simulator

Spiking Neural Network (SNN) simulations require internal variables - such as the membrane voltages of individual neurons and their synaptic inputs - to be updated on a sub-millisecond resolution. As a result, a single second of simulation time requires many thousands of update calculations per neuron. Furthermore, increases in the scale of SNN models have, accordingly, led to manyfold increases in the runtime of SNN simulations. Existing solutions to this problem of scale include high performance CPU based simulators capable of multithreaded execution (\"CPU parallelism\"). More recent GPU based simulators have emerged, which aim to utilise GPU parallelism for SNN execution. We have identified several key speedups, which give GPU based simulators up to an order of magnitude performance increase over CPU based simulators on several benchmarks. We present the Spike simulator with three key optimisations: timestep grouping, active synapse grouping, and delay insensitivity. Combined, these optimisations massively increase the speed of executing a SNN simulation and produce a simulator which is, on a single machine, faster than currently available simulators.

neuroscience↗

bioRxiv2017

Machine Learning-Based State-Of-The-Art Methods For The Classification Of RNA-Seq Data

RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

bioinformatics↗