BOL: Related items

Understanding pango networks !

Abhi — Sat, 16 Oct 2021 14:02:36 -0500

In the vast majority of instances it is expected that Pango lineage names and designations will conform to the following rules. These rules also act as guidelines for the decisions made by the Lineage Designation Committee.

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

https://www.pango.network/how-does-the-system-work/what-are-pango-lineages/

Reference paper

https://www.nature.com/articles/s41564-020-0770-5

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Bioinformatics Training Material !

BioStar — Sat, 18 Mar 2023 11:26:18 -0500

Glittr is a curated list of bioinformatics training material.
All material is:

In a GitHub or GitLab repository
Free to use
Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

New Machine Learning Packages in R

Rahul Nayak — Fri, 27 Mar 2020 12:11:21 -0500

Machine Learning

autokeras v1.0.1: Implements an interface to AutoKeras, an open source software library for automated machine learning. See README for an example.

MTPS v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in Xing et al. (2019). See the vignette to get started.

quanteda.textmodels v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the Laver et al. (2003) wordscores model, the Perry & Benoit’s (2017) class affinity scaling model, and the Slapin & Proksch (2008) wordfish model. See the vignette to get started.

SeqDetect v1.0.7: Implements the automaton model found in Krleža, Vrdoljak & Brčić (2019) to detect and process sequences. See the vignette for examples and theory.

studyStrap v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See Kishida, et al. (2019) for background and the vignette for how to use the package.

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

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:

Pimp your brain: Bioinformatics

Wed, 20 Aug 2014 22:09:21 -0500

Jan Lisec from the Max Planck Institute of Molecular Plant Physiology explains, in this "pimp your brain" episode, what bioinformatics is and why bioinformatics is so important and indispensable for biological research. In the video serial "Pimp your brain" scientists from the Max Planck Institute of Molecular Plant Physiology describe their research. More videos from the 'Pimp your brain' serial are available on www.youtube.com/playlist?list=PL-l9VItC9Gn2Ur2Xj6PTOAkjLUlVPbIOO More videos are available on www.mpimp-golm.mpg.de

NGS teaching material

Jit — Wed, 05 Apr 2017 04:29:06 -0500

High throughput sequencing (HTS) technologies are being applied to a wide range of important topics in biology. However, the analyses of non-model organisms, for which little previous sequence information is available, pose specific problems. This course addresses the specific strengths and weaknesses of alternative HTS technologies, the computational resources needed for HTS, and how to analyze non-model species using HTS. The course consists of a practical training module, HTS bioinformatics training, and lecturing/seminars of HTS approaches specifically targeting non-model organisms.

Address of the bookmark: http://marinetics.org/teaching/hts/Assembly.html

Hello Python World !

Rahul Nayak — Mon, 14 May 2018 16:41:01 -0500

As I mentioned earlier, I will keep on posting one Python script per day to introduce you to Python programming. Whether you are an experienced programmer or not, this tutorial is intended for everyone who wishes to learn the Python programming language.

Python is a very simple language, and has a very straightforward syntax. The simplest directive in Python is the "print" directive - it simply prints out a line (and also includes a newline).

Create a file Hello.py

print("Hello, Python World !.")

Run

python3 Hello.py

Bioinformatics in Africa: Part 4 - Morocco

BioStar — Sat, 06 Feb 2021 13:31:24 -0600

Bioinformatics, in the UFR in Artificial Intelligence and Bioinformatics, deals with the management, the analysis, the modelling and the visualization of biological databases. Since the size of the databases is often exponential, the traditional algorithms are not very effective when seeking for a good computational solution.

To take care of this issue, many ways are opened to the researchers to improve the quality of the algorithms:

1. Usage of new information processing methods like artificial neuronal networks, genetic algorithms, etc. 2. Usage of Data mining to explore biochemical databases,
3. Usage of Machine learning on the biological examples to solve, for example, the problem of classification in Bioinformatics.

UFR offers in addition a doctoral training in Computer Science and Bioinformatics.

Doctoral module which includes: a Diplôme des Etudes Supérieures Approfondies (DESA) of two years; and a doctorate studies program with a national Ph.D. certification. Three specializations constitute the teaching trunk of the ENSAT: Computer engineering, Telecom engineering, and electronic systems engineering.

Research Interest and Activities:

The following are the present areas of research interest:

1. Machine Learning and Profile Gene Expression of Cancer
2. Predicting Protein structure
3. Hidden Markov Models (HMMs) and multiple alignments
4. Transformational Grammar for sequence modelling
5. Physical Mapping: STSs
6. Evolutionary Computation applied to Genomic and Proteomic
7. Predicate Logic and Protein Structure

Web site and links:

http://www.ensat.ac.ma/udiab http://www.pasteur.fr/pasteur/international/annonce_coursBioinfoannonce06_casa.pdf