129 skills found · Page 5 of 5
CSB-KaracaLab / RBD ACE2 MutBenchBenchmarking the structure-based mutation predictors on ace2-rbd binding data set
shengqh / GlmvcSomatic Mutation Calling Using Both DNA and RNAseq Data
EsrafilElahi / React Query Youtube DataSlicesThis repository provides a comprehensive guide on using React Query for efficient data fetching and state management in complex React projects. It includes code examples, best practices, and tips for handling queries, mutations, and other advanced concepts.
kmezhoud / CanceR:chart: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).
karissawhiting / OncokbRAnnotate mutation, copy number alteration and structural variant data in R using oncoKB Annotation API
lucieabergeron / Germline Mutation RatePipeline to estimate germline mutation rate from NGS pedigree data
bbglab / Intogen Plusa framework for automatic and comprehensive knowledge extraction based on mutational data from sequenced tumor samples from patients.
yunlongcaolab / Dms PipelineData processing pipeline for high-throughput deep mutational scanning of mAbs
ttimbers / Million Mutation Project Dye Filling SKATGenomic data and code to accompany the SKAT analysis of Million Mutation Project strains dye-filling phenotypes reported in Timbers et al.
SinOncology / TMB PanelSupplemental data for the manscirpt " Assessment of tumor mutation burden calculation from gene panel sequencing data"
ul-mds / GeckoPython library for the generation and mutation of realistic personal identification data at scale
lkubatko / MOCode and simulation data for the paper "A Phylogenetic Approach to Inferring the Order in Which Mutations Arise during Cancer Progression", co-authored by Yuan Gao, Jeff Gaither, and Julia Chifman
CheRayLiu / QuickShopThe GraphQL API allows users to access and manipulate product data through queries and mutations to perform actions
Computational-Imaging-LAB / IRIS MRS AIIRIS-MRS-AI is a tool that classifies IDH and TERTp mutations in gliomas. Besides these capabilities, IRIS-MRS-AI is a tool that can create custom models using users' data.
bbyc4kes / File DriveIntroducing our File Storage App with Next.js, leveraging Shadcn for decentralized storage, and TypeScript for robust code quality. With Convex and Clerk, manage user roles and permissions efficiently, controlling data flow and mutations seamlessly. Experience streamlined file management with our comprehensive solution.
rimdrira / ABC GAWe present in this project the evaluation of our work. This evaluation is based on several simulations. Our simulation consists of two parts. We set, firstly, the optimal parameters configuration of our ABC-GA algorithm that provides the optimal solution. This part is implemented in the folder validation_test. Second, we evaluate the performance of the ABC-GA algorithm based on several performance metrics. This part is implemented in the folder evaluation_test. The data structure folder present the data structure used in our algorithm to design a composition plan. We implement genetic operation (cross-over and mutation) in the folder genetic_operations. All the simulations carried out in our work are executed on a machine whose characteristics are: •Processor: 2.9 GHz Intel Core i5 dual core. •RAM capacity: 8GB. •Operating system: Mac OS. The development language used in our work is Python. The QoS attributes considered in our simulations are cost,response time, availability and reliability. The simulations performed are based on a variation of the values of theseattributes in order to generate several solutions. We define the laws of variation ofthese attributes as follows: The cost follows the Uniform law between[0.2,0.95]. Response time follows the Uniform law [20,1500]. Availability follows the Uniform law between[0.9,0.99]. Reliability follows the Uniform Law between[0.7,0.95] We assign the weight = 0.25to each quality of service attribute.
Bioconductor / RaggedExperimentMatrix-like representations of mutation and CN data
zzhu33 / ScSplittertool for preprocessing scRNA-seq data for mutation calling
ys08 / Bionexran R package for integrative network-based analysis of gene somatic mutation and gene expression data to identify cancer drivers
pimentellab / LilaceAnalyze FACS-based Deep Mutational Scanning Data
