BOL: Related items

Machine learning in Perl

Jit — Sun, 16 Feb 2020 15:32:03 -0600

this is a fourth blog post in the Machine learning in Perl series, focusing on the AI::MXNet, a Perl interface to Apache MXNet, a modern and powerful machine learning library.

If you're interested in refreshing your memory or just new to the series, please check previous entries over here: 1 2 3

https://metacpan.org/pod/AI::MXNet

Address of the bookmark: http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Shruti Paniwala — Thu, 26 May 2022 00:59:49 -0500

CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB

Thu, 05 Aug 2021 16:11:32 -0500

Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application.

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

The Best Free Crash Course on Large Language Models (LLMs) I've Come Across

Neel — Tue, 04 Aug 2026 04:57:00 -0500

Stanford CME 295: Transformers & Large Language Models course by Afshine Amidi and Shervine Amidi. The official course website contains the syllabus, slides, and links to all lecture recordings.

Official course

Stanford CME 295 – Transformers & Large Language Models

Official YouTube playlist

Stanford Online – CME 295 Playlist

Course schedule

Transformer – Tokenization, embeddings, attention, Transformer architecture
Transformer-Based Models & Tricks – RoPE, MQA/GQA, BERT variants
Large Language Models – GPT, MoE, prompting, Chain-of-Thought
LLM Training – Pretraining, SFT, LoRA, optimization
LLM Tuning – RLHF, DPO, preference tuning
LLM Reasoning – Reasoning models, GRPO, scaling
Agentic LLMs – RAG, tool calling, ReAct, agents
LLM Evaluation – LLM-as-a-Judge, benchmarks, biases
Current Trends & Recap – Vision Transformers, diffusion-based LLMs, future directions

If you're looking for a research-level understanding of LLMs (beyond prompt engineering), this is one of the best freely available university courses currently available. It pairs particularly well with:

scikit-learn

Jitendra Prajapati — Mon, 29 Feb 2016 17:39:24 -0600

Machine Learning in Python

Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

More at http://scikit-learn.org/stable/index.html

Address of the bookmark: http://scikit-learn.org/stable/auto_examples/index.html

R tuorial

Rahul Nayak — Mon, 31 Jul 2017 08:41:40 -0500

R learning resources

https://flowingdata.com/

Address of the bookmark: https://flowingdata.com/

Introduction to Bioinformatics and Computational Biology

Jit — Mon, 25 Jan 2021 01:32:30 -0600

This is the course material for STAT115/215 BIO/BST282 at Harvard University.

Xiaole Shirley Liu (lead instructor)
Joshua Starmer
Martin Hemberg
Ting Wang
Feng Yue

Ming Tang
Yang Liu
Jack Kang
Scarlett Ge
Jiazhen Rong
Phillip Nicol
Maartin De Vries

We thank many colleagues in the community, who helped Dr. Liu in prepare the STAT115/215 BIO/BST282 course over the years.

Address of the bookmark: https://liulab-dfci.github.io/bioinfo-combio/

Bioinformatics in Africa: Part6 - Sudan

BioStar — Sat, 06 Feb 2021 21:20:59 -0600

Commission for Biotechnology & Genetic Engineering Khartoum: The Commission for Biotechnology and Genetic Engineering was established in 9/2/1993 as research unit. In addition to research activities it acts as focal point for the International Center for Biotechnology and Genetic Engineering. The commission conducts researches in order to play a part in solving economical, environmental, health and nutritional problems using modern research techniques with an emphasis on the applied researches in these areas. The laboratories were well furnished with the essential equipments and the catalyst infrastructure to facilitate emergence of a successful for research. The Commission equipped with a computer center and information to serve as informatics and Digital library.

Research Interest and Activities: 1. Plant Genetic Transformations
2. Molecular Population Genetics 3. Detection of human and Animals diseases 4. Breast Cancerspecific protein marker 5. Phytochemical 6. Genomic map 7. Bioremediation 8. Tissue Culture.

Web site and links: www.geocity.cbge.com

Basic Structure of Snakemake Pipeline Run !

Abhi — Thu, 14 Oct 2021 07:01:38 -0500

/user/snakemake-demo$ ls

config.json data envs scripts slurm-240702.out Snakefile

data = mock data for the snakefile to use
Snakefile = name of the snakemake “formula” file
- Note: The default file that snakemake looks for in the current working directory is the Snakefile. If you would like to override that you can specify it following the -s
  - snakemake -s snakefile.py
envs = directory for storing the conda environments that the workflow will use.
scripts = directory for storing python scripts called by the snakemake formula.
config.json = json format file with extra parameters for our snakemake file to use.
cluster.json = json format file with specification for running on the HPC
samples.txt = file we will use later relating to the config.json file.

Run the snakemake file as a dry run (the example workflow shown above).

This will build a DAG of the jobs to be run without actually executing them.
snakemake --dry-run

User can execute rules of interest.

snakemake --dry-run all VS. snakemake --dry-run call VS. snakemake --dry-run bwa

Run the snakemake file in order to produce an image of the DAG of jobs to be run.

snakemake --dag | dot -Tsvg > dag.svg OR snakemake --dag | dot -Tsvg > dag.svg

Run the snakemake (this time not as a dry run)

snakemake --use-conda

Prepare for Coding Interview !

Abhi — Tue, 11 Jan 2022 06:14:41 -0600

This is a comprehensive guide to prepare for your next coding interview. It's great for recent graduates and has questions and practice materials structured from traditional big tech interview formats.

While it does not include the latest developments in programming since 2019, it nails the core fundamentals in a very comprehensive and accessible way!

Credits to Kaiyu Zhang, with additional material in the appendix sourced from Reddit.

People say that interviews at Google will cover as much ground as possible. As a new college graduate, the ground that I must capture are the following. Part of the list is borrowed from a reddit post: https://www. reddit.com/r/cscareerquestions/comments/206ajq/my_onsite_interview_experience_at_google/ #bottom-comments.

1. Data structures

2. Trees and Graph algorithms

3. Dynamic Programming

4. Recursive algorithms

5. Scheduling algorithms (Greedy)

6. Caching 1

7. Sorting

8. Files

9. Computability

10. Bitwise operators

11. System design