9 skills found
nickjcroucher / GubbinsRapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins
TIBHannover / BacDiveRInofficial R client for the DSMZ's Bacterial Diversity Metadatabase (former contact: @katrinleinweber). https://api.bacdive.dsmz.de/client_examples seems to be the official alternatives.
CDCgov / BMGAPThe Bacterial Meningitis Genome Analysis Platform, an analysis pipeline, ExpressJS API, and ReactJS webapp for the analysis and characterization of bacterial meningitis samples
jade-nhri / CCBGpipeA pipeline for completing circular bacterial genomes using a sampling strategy
greenlabjhmi / 2018 Bacterial Pipeline RiboseqRibosome profiling pipeline for bacterial samples created by Fuad Mohammad.
ashm97 / Developing An Infection State Predictive ModelA fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure a given individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection) we conducted a meta-analysis of human blood infection studies using Machine Learning (ML). We focused on publicly available gene expression data from two widely used platforms, Affymetrix and Illumina microarrays as they represented a significant proportion of the available data. We were able to develop multi-class models with high accuracies with our best model predicting 93% of bacterial and 89% viral samples correctly. To compare the selected features in each of the different technologies, we reverse engineered the underlying molecular regulatory network and explored the neighbourhood of the selected features. This highlighted that although on the gene-level the models differed, they did contain genes from the same areas of the network. Specifically, this convergence was to pathways including the Type I interferon Signalling Pathway, Chemotaxis, Apoptotic Processes, and Inflammatory / Innate Response.
gsiekaniec / ORIORI (Oxford nanopore Reads Identification) is a software allowing, from long nanopore reads, to identify the bacterial strains present in a sample.
DevyanshMalhotra / MangoLeafDiseaseDetectionThis project uses the ResNet50 model to detect and classify mango diseases from images, including Anthracnose and Bacterial Canker. It includes data preparation, model training, evaluation code, and sample outputs. Ideal for early detection and management of mango diseases.
shubhi0168 / PLANet Plant Leaf Analyser Network It is observed that a lot of crop gets wasted every year due to the illness spreading (reasons can be pests, bacterial or fungal infections, etc.) in the farms. Farmers face huge losses because of the lack of a handy and economical technology that can detect such diseases in the initial stages of occurrence. This system can prove a remedy to this issue by developing a prototype that recognizes the same at an early stage and thence providing them a way to cure it using appropriate pesticides, insecticides, etc. �The aim is to build a deployable system for plants’ disease detection by training a Convolutional Neural Network model that traces a particular disease in a particular plant species. It detects the plant disease by scanning the leaves of the species as it is trained for those illness occurring on the leaves.Leaf plays a major role in providing important information related to the health of a plant. This method is based on a technique that scans the leaf sample of the infected plant. These scanned samples will then be processed and the model will be trained to classify the diseases accordingly�. �
