BOL: Related items

Free Books on Machine Learning and Artificial Intelligent !

BioStar — Thu, 16 Mar 2023 00:10:24 -0500

An Introduction to Statistical Learning
This book provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

https://hastie.su.domains/ISLR2/ISLRv2_website.pdf

Python Data Science Handbook
You’ll learn how to use the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. This resource is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models.

https://jakevdp.github.io/PythonDataScienceHandbook/

Dive into Deep Learning
Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 400 universities from 60 countries

https://d2l.ai/

Approaching (Almost) Any Machine Learning Problem
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is for you if you are looking for guidance on approaching machine learning problems.

https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf

AI Uncovers Hidden Secrets in Bacterial DNA, Opening New Frontiers in Genomic Research

Jitendra Narayan — Fri, 25 Sep 2026 22:38:42 -0500

Scientists are now using artificial intelligence in order to examine sections of bacterial DNA that have not been looked at before. This method is showing potential RNA interactions and previously unknown genetic systems which could alter our understanding of microbes. A new study presents Minerva, a genome language model, demonstrating that AI can assist researchers in identifying biological patterns that traditional techniques might fail to detect.

The study, which was made available as a preprint on bioRxiv on 23 September 2026, focuses on the non-coding sections of DNA that are still largely unknown. Although these areas do not produce proteins, they can contain important signals and instructions which have an effect on cell function. The research presents a novel approach to investigating how bacteria handle and control their genetic information.

AI maps previously unexplored genomic regions

The team developed Minerva in order to identify possible interactions between different regions in microbial genomes; rather than depending on similarities with known sequences, Minerva predicts these relationships directly from the DNA by using patterns learned by a genome language model.

On 150 bacterial genomes, Minerva identified a large number of interactions that were not included in the existing annotations. The researchers stated that 84.3 per cent of the predicted intergenic base-pairing interactions were not present in the current annotations, which demonstrates that AI can be of help in generating new ideas in biology.

Unusual RNA structures and viral genetic systems identified

The study also examined a bacterial RNA family known as TwoAYGGAY in Pseudomonas; the model anticipated longer RNA structures and identified associations with repeated DNA sequences, thus providing new insights into how these non-coding elements are organised and how they have evolved.

In a separate section of the study, the researchers examined reverse transcriptase systems associated with bacteriophages, which are viruses that infect bacteria. They identified RNA arrays that maintain their structure but have different sequences and were linked to Unknown Group 27 reverse transcriptases. The findings indicate that these RNAs could function as templates for the production of complementary DNA that is capable of forming hairpin shapes.

The researchers also observed that Minerva was able to detect patterns associated with protein-coding areas, even though it had not been trained to do so. This indicates that genome language models may pick up on biological signals that go beyond what they were intended to identify.

Implications for future genomic research

The fact that artificial intelligence is becoming increasingly important in the field of microbial genomics is shown by the fact that models such as Minerva are able to predict interactions and identify patterns in areas which have not been extensively studied, thus helping researchers to decide what to study next and enabling them to gain a better understanding of biological systems that are still not well understood.

Yet the predictions do not reveal the exact function of each element identified. In order to verify which of the predicted interactions actually take place in living cells and the way in which they affect the microbes, experiments will be necessary. Although the study has undergone peer review, it does nonetheless offer a promising illustration of how machine learning can complement traditional genomics and assist scientists in moving from the identification of known genes to the exploration of the complex relationships that shape microbial life.

More at https://www.biorxiv.org/content/10.64898/2026.09.22.753630v2.full.pdf

Janggu - Deep learning for Genomics

Jit — Tue, 23 Mar 2021 05:14:43 -0500

Janggu is a python package that facilitates deep learning in the context of genomics. The package is freely available under a GPL-3.0 license.

Detail tutorial at https://janggu.readthedocs.io/en/latest/

USE cases

https://github.com/wkopp/janggu_usecases

Address of the bookmark: https://github.com/BIMSBbioinfo/janggu

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

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

modelStudio: a package automates the explanation of machine learning predictive models

Jit — Wed, 22 Jan 2020 23:58:11 -0600

The modelStudio package automates the explanation of machine learning predictive models. This package generates advanced interactive and animated model explanations in the form of a serverless HTML site.

It combines R with D3.js to produce plots and descriptions for various local and global explanations. Tools for model exploration unite with tools for EDA (Exploratory Data Analysis) to give a broad overview of the model behavior. modelStudio is a fast and condensed way to get all the answers without much effort. Break down your model and look into its ingredients with only a few lines of code.

Address of the bookmark: https://modeloriented.github.io/modelStudio/index.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

Suisse Life Science Group

Rahul Agarwal — Sun, 07 Jan 2018 14:42:23 -0600

THE WORLD’S MOST UNIQUE HEALTH & WELLNESS SERVICE:

AI and science working together to manage the root causes of your aging

Personalized plan built from your biomarkers and devices

Biologically-active treatments (cellular health). No drugs.

Source is Linkedln link :

https://www.linkedin.com/company/5143768/

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

NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design

BioStar — Fri, 21 Feb 2025 10:39:47 -0600

NVIDIA and the Arc Institute have introduced Evo 2, a groundbreaking AI model designed to understand, predict, and generate DNA sequences. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

Computational Genomics: Applied Comparative Genomics

Jit — Wed, 29 Nov 2017 05:11:30 -0600

The primary goal of the course is for students to be grounded in theory and leave the course empowered to conduct independent genomic analyses. We will study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. There are no formal course prerequisites, although the course will require familiarity with UNIX scripting and/or programming to complete the assignments and course project.

Address of the bookmark: https://github.com/schatzlab/appliedgenomics

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