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irods

Open Source Data Management Software

isa-l

Intelligent Storage Acceleration Library

janggu

Deep learning infrastructure for genomics

java

All Algorithms implemented in Java

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

jemalloc

jmbayes2

Extended Joint Models for Longitudinal and Survival Data

jsat

Java Statistical Analysis Tool, a Java library for Machine Learning

json

JSON for Modern C++

kalign

A fast multiple sequence alignment program.

kallisto

Near-optimal RNA-Seq quantification

kernlab

:exclamation: This is a read-only mirror of the CRAN R package repository. kernlab — Kernel-Based Machine Learning Lab

key-book

《机器学习理论导引》（宝箱书）的证明、案例、概念补充与参考文献讲解。在线阅读地址：http://keybook.datawhale.club

khmer

In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more

kindred

A Python biomedical relation extraction package that uses a supervised approach (i.e. needs training data).

klib

A standalone and lightweight C library

kmc

Fast and frugal disk based k-mer counter

kmcp

Accurate metagenomic profiling && Fast large-scale sequence/genome searching

kmtricks

modular k-mer count matrix and Bloom filter construction for large read collections

kraken2

The second version of the Kraken taxonomic sequence classification system

krakenuniq

KrakenUniq: Metagenomics classifier with unique k-mer counting for more specific results

ksw2

Global alignment and alignment extension

ktrim

Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data

lambda

LAMBDA – the Local Aligner for Massive Biological DatA

lancet

Microassembly based somatic variant caller for NGS data

langchain

⚡ Building applications with LLMs through composability ⚡

lapack

LAPACK development repository

last

find & align related regions of sequences for moderately large data (e.g. genomes, DNA reads, proteomes).

lastz

Program for aligning DNA sequences, a pairwise aligner.

latent-spectral-models

About Code Release for "Solving High-Dimensional PDEs with Latent Spectral Models" (ICML 2023), https://arxiv.org/abs/2301.12664