A multi-task CNN learning model for taxonomic assignment of human viruses

Background Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. Results In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. Conclusions We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus.

An empirical Bayesian ranking method, with applications to high throughput biology

Motivation In bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a P-value-based ranking of locations which might then be investigated further in follow-up experiments. However, this strategy may result in small effect sizes, with low P-values, being ranked more favorably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available. Results We develop an Empirical Bayes ranking algorithm, using the marginal distribution of the data over all locations to estimate an appropriate prior. In simulations and analysis using real datasets, we demonstrate favorable performance compared to ordering P-values and a number of other competing ranking methods. The algorithm is computationally efficient and can be used to rank the entirety of genomic locations or to rank a subset of locations, pre-selected via traditional FWER/FDR methods in a 2-stage analysis. Availability and implementation An R-package, EBrank, implementing the ranking algorithm is available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

Domain and Intelligence Based Multimedia Question Answering System

Multimedia question answering systems have become very popular over the past few years. It allows users to share their thoughts by answering given question or obtain information from a set of answered questions. However, existing QA systems support only textual answer which is not so instructive for many users. The user’s discussion can be enhanced by adding suitable multimedia data. Multimedia answers offer intuitive information with more suitable image, voice and video. This system includes a set of information as well as classification of question and answer, query generation, multimedia data selection and presentation. This system will take all kinds of media such as text, images, videos, and videos which will be combined with a textual answer. In a way, it automatically collects information from the user to improvising the answer. This method consists of ranking for answers to select the best answer. By dealing out a huge set of QA pairs and adding them to a database, multimedia question answering approach for users which finds multimedia answers by matching their questions with those in the database. The effectiveness of Multimedia system is determined by ranking of text, image, audio and video in users answer. The answer which is given by the user it’s processed by Semantic match algorithm and the best answers can be viewed by Naive Bayesian ranking system.