Wrecking sandwich traders for fun and profit

SALMONN family: A suite of advanced multi-modal LLMs

RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment

Moved to Codeberg

A simple monitoring system.

🦑 Splatoon 3 map rotations, Salmon Run schedules, SplatNet gear, and more

Tasty code, lightly smoked for extra flavor. No fishy business

video-SALMONN 2 is a powerful audio-visual large language model (LLM) that generates high-quality audio-visual video captions, which is developed by the Department of Electronic Engineering at Tsinghua University and ByteDance.

Self-Alignment with Principle-Following Reward Models

salmon uploading tools~

Segmentation deep learning ALgorithm based on MONai toolbox: single and multi-label segmentation software developed by QIMP team-Vienna.

An OStatus library for node-js. Implementing webfinger, PubSubHubBub, hcard, atom/json activitystreams and salmon. It serves as the building block of a decentralized social network.

SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances

No description available

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

No description available

The official code for the SALMon🍣 benchmark (ICASSP 2025 - Oral)

Prepare Sailfish and Salmon output for downstream analysis

No description available

Useful tools for working with Salmon output

Response to blog post about Salmon

An open-source, self-hosted, and non-custodial solution for receiving cryptocurrency donations.

[WIP] Web Crawler in Node.js to spider dynamically whole websites.

A Ruby toolset for interacting with the OStatus suite of protocols

Differential expression and allelic analysis, nonparametric statistics

PISCES is a pipeline for rapid transcript quantitation, genetic fingerprinting, and quality control assessment of RNAseq libraries using Salmon.

RNAseq pipeline centered on Salmon

Self-custodial, open-source & community-owned Solana wallet