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Scientists Develop Automated Machine Learning System for Biology Research – ReadWrite

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In a groundbreaking study, researchers at the Massachusetts Institute of Technology (MIT) have developed an automated machine learning system called BioAutoMATED that can generate AI models for biology research. Led by Jim Collins, the Termeer Professor of Medical Engineering and Science, the team aims to simplify the process of building machine learning models for scientists and engineers in the field of biology. This innovative system not only selects and builds appropriate models for given datasets but also handles the laborious task of data preprocessing. By reducing the time and effort required, BioAutoMATED opens up new possibilities for researchers in the biological sciences.

Recruiting machine learning experts can be a time-consuming and costly process for science and engineering labs. Even with an expert on board, selecting the right model, formatting the dataset, and fine-tuning the model can significantly impact its performance. According to a Google course on the Foundations of Machine Learning, data preparation and transformation alone can take up to 80% of the project time. This hurdle often discourages researchers from utilizing machine learning techniques in biology.

BioAutoMATED is an automated machine learning system specifically designed for biology research. While automated machine learning (AutoML) systems are still relatively new, with most applications focused on image and text recognition, BioAutoMATED extends the capabilities of AutoML to biological sequences. This is significant because the fundamental language of biology is based on sequences such as DNA, RNA, proteins, and glycans.

One of the key advantages of BioAutoMATED is its ability to explore and build various types of supervised ML models. These include binary classification models, multi-class classification models, and regression models. By incorporating multiple tools under one umbrella, BioAutoMATED provides a larger search space than individual AutoML tools, allowing for more flexibility and accuracy in model selection.

Traditionally, conducting experiments at the intersection of biology and machine learning has been a costly endeavor. Research groups often have to invest in significant digital infrastructure and trained human resources before they can determine if their ideas are viable. BioAutoMATED aims to lower these barriers by providing researchers with the freedom to run initial experiments and assess the feasibility of further experimentation. This way, they can determine if it’s worthwhile to hire a machine learning expert to build a different model for their research.

The benefits of using BioAutoMATED are manifold. Firstly, it significantly reduces the time and effort required to build AI models for biology research. What would typically take weeks of effort can now be accomplished in just a few hours. This time-saving allows researchers to focus more on their core research objectives rather than getting caught up in the technicalities of machine learning.

Secondly, BioAutoMATED is particularly advantageous for research groups with smaller, sparser biological datasets. It can explore models that are better-suited for such datasets, as well as more complex neural networks. This versatility ensures that researchers can make the most of their available data and obtain meaningful insights.

To promote widespread adoption and collaboration, the researchers have made the code for BioAutoMATED publicly available on GitHub. They encourage others to improve upon their work and collaborate with larger communities to make BioAutoMATED a tool for all. By generating awareness and merging biological practice with fast-paced AI-ML practice, BioAutoMATED aims to advance the field of biology research.

BioAutoMATED represents a significant breakthrough in the field of biology research. By automating the process of generating AI models, this innovative system empowers scientists and engineers to leverage machine learning for their research. With its ability to select appropriate models and handle data preprocessing, BioAutoMATED streamlines the research process and reduces the barriers to entry for researchers in the biological sciences. As the field continues to evolve, the possibilities for collaboration and discovery are endless.

First reported on MIT News

Frequently Asked Questions

Q: What is BioAutoMATED?

A: BioAutoMATED is an automated machine learning system developed by researchers at MIT for biology research. It simplifies the process of building machine learning models for scientists and engineers by automating model selection and data preprocessing.

Q: What is the goal of BioAutoMATED?

A: The goal of BioAutoMATED is to reduce the time and effort required to build AI models for biology research. It aims to make machine learning techniques more accessible to researchers in the biological sciences.

Q: How does BioAutoMATED differ from traditional machine learning approaches?

A: BioAutoMATED is an automated machine learning system specifically designed for biology research. It extends the capabilities of automated machine learning (AutoML) to biological sequences such as DNA, RNA, proteins, and glycans. It explores and builds various types of supervised ML models, providing researchers with a larger search space for model selection.

Q: What are the advantages of using BioAutoMATED?

A: BioAutoMATED significantly reduces the time and effort required to build AI models for biology research, allowing researchers to focus more on their core objectives. It is particularly advantageous for research groups with smaller, sparser biological datasets, as it can explore models better-suited for such datasets and complex neural networks.

Q: How does BioAutoMATED lower the barriers to entry for researchers?

A: BioAutoMATED allows researchers to run initial experiments and assess the feasibility of further experimentation without the need for significant digital infrastructure or trained machine learning experts. It enables researchers to determine if it’s worthwhile to invest in additional machine learning expertise for their research.

Q: Is BioAutoMATED freely available to the public?

A: Yes, the code for BioAutoMATED has been made publicly available on GitHub. The researchers encourage others to improve upon their work and collaborate to make BioAutoMATED a tool for all. They aim to promote widespread adoption and collaboration in the field of biology research.

Q: What are the potential implications of BioAutoMATED for biology research?

A: BioAutoMATED represents a significant breakthrough in biology research by automating the process of generating AI models. It empowers scientists and engineers to leverage machine learning techniques more effectively, streamlining the research process and reducing barriers to entry. It has the potential to advance the field of biology research and foster collaboration and discovery.

John Boitnott

John Boitnott is a news anchor at ReadWrite. Boitnott has worked at TV News Anchor, print, radio and Internet companies for 25 years. He’s an advisor at StartupGrind and has written for BusinessInsider, Fortune, NBC, Fast Company, Inc., Entrepreneur and Venturebeat. You can see his latest work on his blog, John Boitnott

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Here’s how Helene and other storms dumped a whopping 40 trillion gallons of rain on the South

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More than 40 trillion gallons of rain drenched the Southeast United States in the last week from Hurricane Helene and a run-of-the-mill rainstorm that sloshed in ahead of it — an unheard of amount of water that has stunned experts.

That’s enough to fill the Dallas Cowboys’ stadium 51,000 times, or Lake Tahoe just once. If it was concentrated just on the state of North Carolina that much water would be 3.5 feet deep (more than 1 meter). It’s enough to fill more than 60 million Olympic-size swimming pools.

“That’s an astronomical amount of precipitation,” said Ed Clark, head of the National Oceanic and Atmospheric Administration’s National Water Center in Tuscaloosa, Alabama. “I have not seen something in my 25 years of working at the weather service that is this geographically large of an extent and the sheer volume of water that fell from the sky.”

The flood damage from the rain is apocalyptic, meteorologists said. More than 100 people are dead, according to officials.

Private meteorologist Ryan Maue, a former NOAA chief scientist, calculated the amount of rain, using precipitation measurements made in 2.5-mile-by-2.5 mile grids as measured by satellites and ground observations. He came up with 40 trillion gallons through Sunday for the eastern United States, with 20 trillion gallons of that hitting just Georgia, Tennessee, the Carolinas and Florida from Hurricane Helene.

Clark did the calculations independently and said the 40 trillion gallon figure (151 trillion liters) is about right and, if anything, conservative. Maue said maybe 1 to 2 trillion more gallons of rain had fallen, much if it in Virginia, since his calculations.

Clark, who spends much of his work on issues of shrinking western water supplies, said to put the amount of rain in perspective, it’s more than twice the combined amount of water stored by two key Colorado River basin reservoirs: Lake Powell and Lake Mead.

Several meteorologists said this was a combination of two, maybe three storm systems. Before Helene struck, rain had fallen heavily for days because a low pressure system had “cut off” from the jet stream — which moves weather systems along west to east — and stalled over the Southeast. That funneled plenty of warm water from the Gulf of Mexico. And a storm that fell just short of named status parked along North Carolina’s Atlantic coast, dumping as much as 20 inches of rain, said North Carolina state climatologist Kathie Dello.

Then add Helene, one of the largest storms in the last couple decades and one that held plenty of rain because it was young and moved fast before it hit the Appalachians, said University of Albany hurricane expert Kristen Corbosiero.

“It was not just a perfect storm, but it was a combination of multiple storms that that led to the enormous amount of rain,” Maue said. “That collected at high elevation, we’re talking 3,000 to 6000 feet. And when you drop trillions of gallons on a mountain, that has to go down.”

The fact that these storms hit the mountains made everything worse, and not just because of runoff. The interaction between the mountains and the storm systems wrings more moisture out of the air, Clark, Maue and Corbosiero said.

North Carolina weather officials said their top measurement total was 31.33 inches in the tiny town of Busick. Mount Mitchell also got more than 2 feet of rainfall.

Before 2017’s Hurricane Harvey, “I said to our colleagues, you know, I never thought in my career that we would measure rainfall in feet,” Clark said. “And after Harvey, Florence, the more isolated events in eastern Kentucky, portions of South Dakota. We’re seeing events year in and year out where we are measuring rainfall in feet.”

Storms are getting wetter as the climate change s, said Corbosiero and Dello. A basic law of physics says the air holds nearly 4% more moisture for every degree Fahrenheit warmer (7% for every degree Celsius) and the world has warmed more than 2 degrees (1.2 degrees Celsius) since pre-industrial times.

Corbosiero said meteorologists are vigorously debating how much of Helene is due to worsening climate change and how much is random.

For Dello, the “fingerprints of climate change” were clear.

“We’ve seen tropical storm impacts in western North Carolina. But these storms are wetter and these storms are warmer. And there would have been a time when a tropical storm would have been heading toward North Carolina and would have caused some rain and some damage, but not apocalyptic destruction. ”

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Follow AP’s climate coverage at https://apnews.com/hub/climate

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Follow Seth Borenstein on Twitter at @borenbears

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Associated Press climate and environmental coverage receives support from several private foundations. See more about AP’s climate initiative here. The AP is solely responsible for all content.

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‘Big Sam’: Paleontologists unearth giant skull of Pachyrhinosaurus in Alberta

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It’s a dinosaur that roamed Alberta’s badlands more than 70 million years ago, sporting a big, bumpy, bony head the size of a baby elephant.

On Wednesday, paleontologists near Grande Prairie pulled its 272-kilogram skull from the ground.

They call it “Big Sam.”

The adult Pachyrhinosaurus is the second plant-eating dinosaur to be unearthed from a dense bonebed belonging to a herd that died together on the edge of a valley that now sits 450 kilometres northwest of Edmonton.

It didn’t die alone.

“We have hundreds of juvenile bones in the bonebed, so we know that there are many babies and some adults among all of the big adults,” Emily Bamforth, a paleontologist with the nearby Philip J. Currie Dinosaur Museum, said in an interview on the way to the dig site.

She described the horned Pachyrhinosaurus as “the smaller, older cousin of the triceratops.”

“This species of dinosaur is endemic to the Grand Prairie area, so it’s found here and nowhere else in the world. They are … kind of about the size of an Indian elephant and a rhino,” she added.

The head alone, she said, is about the size of a baby elephant.

The discovery was a long time coming.

The bonebed was first discovered by a high school teacher out for a walk about 50 years ago. It took the teacher a decade to get anyone from southern Alberta to come to take a look.

“At the time, sort of in the ’70s and ’80s, paleontology in northern Alberta was virtually unknown,” said Bamforth.

When paleontogists eventually got to the site, Bamforth said, they learned “it’s actually one of the densest dinosaur bonebeds in North America.”

“It contains about 100 to 300 bones per square metre,” she said.

Paleontologists have been at the site sporadically ever since, combing through bones belonging to turtles, dinosaurs and lizards. Sixteen years ago, they discovered a large skull of an approximately 30-year-old Pachyrhinosaurus, which is now at the museum.

About a year ago, they found the second adult: Big Sam.

Bamforth said both dinosaurs are believed to have been the elders in the herd.

“Their distinguishing feature is that, instead of having a horn on their nose like a triceratops, they had this big, bony bump called a boss. And they have big, bony bumps over their eyes as well,” she said.

“It makes them look a little strange. It’s the one dinosaur that if you find it, it’s the only possible thing it can be.”

The genders of the two adults are unknown.

Bamforth said the extraction was difficult because Big Sam was intertwined in a cluster of about 300 other bones.

The skull was found upside down, “as if the animal was lying on its back,” but was well preserved, she said.

She said the excavation process involved putting plaster on the skull and wooden planks around if for stability. From there, it was lifted out — very carefully — with a crane, and was to be shipped on a trolley to the museum for study.

“I have extracted skulls in the past. This is probably the biggest one I’ve ever done though,” said Bamforth.

“It’s pretty exciting.”

This report by The Canadian Press was first published Sept. 25, 2024.

The Canadian Press. All rights reserved.

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The ancient jar smashed by a 4-year-old is back on display at an Israeli museum after repair

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TEL AVIV, Israel (AP) — A rare Bronze-Era jar accidentally smashed by a 4-year-old visiting a museum was back on display Wednesday after restoration experts were able to carefully piece the artifact back together.

Last month, a family from northern Israel was visiting the museum when their youngest son tipped over the jar, which smashed into pieces.

Alex Geller, the boy’s father, said his son — the youngest of three — is exceptionally curious, and that the moment he heard the crash, “please let that not be my child” was the first thought that raced through his head.

The jar has been on display at the Hecht Museum in Haifa for 35 years. It was one of the only containers of its size and from that period still complete when it was discovered.

The Bronze Age jar is one of many artifacts exhibited out in the open, part of the Hecht Museum’s vision of letting visitors explore history without glass barriers, said Inbal Rivlin, the director of the museum, which is associated with Haifa University in northern Israel.

It was likely used to hold wine or oil, and dates back to between 2200 and 1500 B.C.

Rivlin and the museum decided to turn the moment, which captured international attention, into a teaching moment, inviting the Geller family back for a special visit and hands-on activity to illustrate the restoration process.

Rivlin added that the incident provided a welcome distraction from the ongoing war in Gaza. “Well, he’s just a kid. So I think that somehow it touches the heart of the people in Israel and around the world,“ said Rivlin.

Roee Shafir, a restoration expert at the museum, said the repairs would be fairly simple, as the pieces were from a single, complete jar. Archaeologists often face the more daunting task of sifting through piles of shards from multiple objects and trying to piece them together.

Experts used 3D technology, hi-resolution videos, and special glue to painstakingly reconstruct the large jar.

Less than two weeks after it broke, the jar went back on display at the museum. The gluing process left small hairline cracks, and a few pieces are missing, but the jar’s impressive size remains.

The only noticeable difference in the exhibit was a new sign reading “please don’t touch.”

The Canadian Press. All rights reserved.

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