#machine learning algorithms

Co-worker: I really like Black Mirror, it would really suck to live in a world like that tho.

Blisters, Fingers, Feet

There's so much going on inside living cells that scientists often face a choice – study the genes and proteins that produce chemical signals on the inside, or focus on changes in growth, movement or shape (morphology) from the outside. A new set of computer techniques links the two approaches together, here using machine learning to spot changes on the surface of three cells, while keeping tabs on chemical signals or ‘pathways’ firing inside. Highlighted in bright colours, ball-like blebs appear like blisters on the surface of a human cancer cell (left), usually indicating imminent self-destruction. Filopodia are finger-like projections of a moving human lung cell (middle), while lamellipodia are ‘feet’ that help a cell from the immune system (right) crawl around sharing information during an infection. Such automated analysis will help cell biologists and clinicians to make sense of the vast amount of information revealed using sensitive light-sheet microscopy.

Written by John Ankers

Image from work by Meghan K Driscoll and colleagues

UT Southwestern Medical Center, Dallas, TX, USA

Originally published under a Creative Commons Licence (BY 4.0)

Published in BioRxiv, July 2018

Cloud of Genes

Biologists strive to understand life, but often they find themselves swamped with information. Studies of how genes switch on or off, known as gene expression, sometimes examine hundreds of genes at once, inside millions of single cells – useful information, but a headache to interpret. A new set of software, called Scanpy, applies automated analysis to these mountains of data. Looking like impressionist art, 1.3 million mouse brain cells are grouped or 'clustered' into colours and locations on this chart. Each cluster might correspond to changes in a different gene, or a relationship between different cells. But it’s not all about producing pretty graphs – patterns in this cloud of numbers may give clues to treating diseases, and Scanpy allows computers to learn to spot them. Machine learning algorithms can be plugged in to take the pressure of biologists surrounded by life’s complexity.

Written by John Ankers

Image from work by F. Alexander Wolf, Philipp Angerer and Fabian J. Theis

Helmholtz Zentrum München – German Research Center for Environmental Health, Institute of Computational Biology, Munich, Germany

Image originally published under a Creative Commons Licence (BY 4.0)

Published in Genome Biology, February 2018

Machine Learning Algorithms: The Technology Behind Smart Systems

Artificial Intelligence is reshaping industries, and Machine Learning Algorithms are one of the most important technologies driving this transformation. These algorithms help computer systems learn from data, identify patterns, and improve performance without needing explicit programming for every task.

Today, Machine Learning Algorithms power many digital applications including recommendation systems, virtual assistants, fraud detection tools, and predictive analytics platforms. As technology continues evolving, understanding these algorithms has become valuable for students, developers, and businesses.

What Are Machine Learning Algorithms?

Machine Learning Algorithms are mathematical models designed to help computers learn from information and make decisions. Instead of relying only on predefined instructions, these systems analyze data and continuously improve accuracy through experience.

The major categories of Machine Learning Algorithms include:

Supervised Learning

These algorithms use labeled datasets where correct outputs are already known.

Examples:

Linear Regression

Decision Trees

Random Forest

Unsupervised Learning

These algorithms discover hidden patterns in data without predefined labels.

Examples:

K-Means Clustering

Principal Component Analysis (PCA)

Reinforcement Learning

These algorithms learn through rewards and penalties.

Applications:

Robotics

Autonomous vehicles

Game AI systems

Benefits of Machine Learning Algorithms

Organizations use Machine Learning Algorithms because they provide several advantages:

Faster processing of large data sets

Improved prediction accuracy

Better decision-making

Reduced manual work

Increased efficiency and productivity

Applications of Machine Learning Algorithms

Common real-world applications include:

Medical diagnosis systems

Product recommendation engines

Fraud detection tools

Customer behavior analysis

Personalized education platforms