AI Research

How AI is helping researchers to demystify gut bacteria

Published

3 days ago

July 4, 2025

VBayesMM uses paired microbiome-metabolite data, with microbial species as input variables and metabolite abundances as target variables. Credit: Briefings In Bioinformatics (2025). DOI: 10.1093/bib/bbaf30

Gut bacteria are known to be a key factor in many health-related concerns. However, the number and variety of them is vast, as are the ways in which they interact with the body’s chemistry and each other.

For the first time, researchers from the University of Tokyo have used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset of gut bacteria in order to find relationships that current analytical tools could not reliably identify.

The human body includes about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. Technically, you’re carrying around more cells that aren’t part of you than are. These gut bacteria are of course responsible for some aspects of digestion, though what’s surprising to some is how they can relate to many other aspects of human health as well.

The bacteria are incredibly varied and also produce and modify a bewildering number of different chemicals called metabolites. These act like molecular messengers, permeating your body, affecting everything from your immune system and metabolism to your brain function and mood. Needless to say, there’s much to gain by understanding gut bacteria.

“The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,” said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences.

“By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.”

Demystifying gut bacteria with AI — A simplified breakdown of the inputs, process and outputs that make up the system. Credit: *Briefings In Bioinformatics* (2025). DOI: 10.1093/bib/bbaf30

This sounds good, so what’s the problem? As mentioned, there are uncountably many and varied bacteria and metabolites, and therefore far more relationships between these things. Gathering data on this alone is a monumental undertaking, but unpicking that data to find interesting patterns that might betray some useful function is even more so. To do this, Dang and his team decided to explore the use of state-of-the-art artificial intelligence (AI) tools.

“Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers,” said Dang.

“When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns.”

As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimized to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost. However, as time goes on, this will become less and less of a barrier to those wishing to use them.

Other limitations at present include that the system benefits from having more data about the gut bacteria than the metabolites they produce; when there’s insufficient bacteria data, the accuracy drops. Also, VBayesMM assumes the microbes act independently, but in reality, gut bacteria interact in an incredibly complex number of ways.

“We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet,” said Dang. “We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial ‘family tree’ relationships to make better predictions, and further reducing the computational time needed for analysis.

For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications.”

More information:
Dang Tung et al, VBayesMM Variational Bayesian neural network to prioritize important relationships of high dimensional microbiome multiomics data, Briefings In Bioinformatics (2025). DOI: 10.1093/bib/bbaf300

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The Greatest First Basemen of All Time According to Artificial Intelligence

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July 7, 2025

Joe González

In the intricate dance of Major League Baseball, the first baseman stands as a unique blend of offensive powerhouse and defensive anchor. They are the receivers of throws, the stretchers for outs, and often, the most prolific sluggers in the lineup. But who among these giants of the diamond truly represents the pinnacle of the position? Leveraging vast datasets of offensive metrics, defensive prowess, awards, and historical impact, Artificial Intelligence has meticulously analyzed the MLB careers of baseball’s greatest first basemen. The result is a definitive ranking of the top, based on an impartial assessment of their unparalleled contributions to the game.

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I asked ChatGPT to help me pack for my vacation – try this awesome AI prompt that makes planning your travel checklist stress-free

Published

2 hours ago

July 7, 2025

John-Anthony Disotto

It’s that time of year again, when those of us in the northern hemisphere pack our sunscreen and get ready to venture to hotter climates in search of some much-needed Vitamin D.

Every year, I book a vacation, and every year I get stressed as the big day gets closer, usually forgetting to pack something essential, like a charger for my Nintendo Switch 2, or dare I say it, my passport.

This year, however, I’ve got a trick up my sleeve: an incredibly well-engineered ChatGPT prompt that takes into account everything AI knows about me to create the perfect travel-item checklist.

This prompt is super-easy to use, and all you need is access to ChatGPT (it should also work with your AI chatbot of choice).

Here’s how to use ChatGPT to create a travel checklist just for you…

Click here to reveal the full prompt

The prompt: Simply copy and paste the full block of text into ChatGPT, and then respond with the details it asks for. You’ll need to provide your age, destination, and details of your trip for the best results. I’ve also embedded the Reddit post below.

You are a detail-oriented travel assistant and logistics expert. You are helping a user prepare for an upcoming trip by generating a personalized and complete travel accessories checklist. The checklist should consider user-specific details such as age, gender, travel duration, destination climate, activity type, and personal needs. 1. Based on the user’s input, categorize the trip into one of the common types (e.g., leisure, adventure, business, family, romantic). 2. Use the data to: – Determine the expected weather, terrain, and activity levels. – Suggest ideal clothing combinations (layering if needed), footwear, and sleepwear. – Provide tech, toiletry, health, comfort, and safety essentials. – Recommend a luggage type (e.g. hard shell carry-on, backpack, checked-in spinner, duffel) based on the trip length and volume of gear required. – Add unique extras (e.g. swimwear, camera gear, hiking poles, outlet adapters) specific to the destination or travel type. 3. Organize the checklist by categories and include a short summary of why each major group of items is important. 4. For added value, suggest one overlooked item that most travelers forget based on the trip profile. – Keep the tone friendly but professional. – Do not assume any travel preferences not stated by the user. – Be concise but specific; don’t list vague items like “shoes” — specify “waterproof trail shoes” or “casual slip-ons”. – Destination: – Duration: – Gender: – Age: – Trip Type: – Climate: [Type and reason why this luggage is suitable] 1. CLOTHING 2. FOOTWEAR 3. TOILETRIES & GROOMING 4. TECH & ACCESSORIES 5. COMFORT & HEALTH 6. DOCUMENTS & MONEY 7. EXTRAS [Name of the item + short rationale] Apply Theory of Mind to analyze the user’s request, considering both logical intent and emotional undertones. Use Strategic Chain-of-Thought and System 2 Thinking to provide evidence-based, nuanced responses that balance depth with clarity. Reply with: “Please enter your travel information (gender, age, destination, climate, travel days, travel type, any special needs) and I will start the process,” then wait for the user to provide their specific travel process request.

This Prompt Makes a Travel Checklist Just for You (Based on Your Age, Trip & Destination) from r/ChatGPTPromptGenius

Travel made easy

This fantastic prompt was made by u/EQ4C on Reddit, who has a wide range of posts detailing different ways to use ChatGPT.

I found this travel checklist prompt very useful, as it created a full item itinerary based on my week-long vacation. Not only was ChatGPT able to tailor the results based on my exact needs (I detailed what tech products were essential for my travels), but it also offered suggestions on exactly what kind of clothing to bring based on the kind of adventures my fiancée and I are planning to go on.

As massive foodies, our vacations usually involve a few higher-end restaurants, and ChatGPT was able to break down my wardrobe to offer specific business-casual outfits.

I will say, this prompt is only as good as the information you give it, so if you want to rely on ChatGPT to make your packing stress-free you’ll need to offer as much detail as possible. One useful enhancement would be to take a photo of the clothes in your wardrobe, so the AI can piece together outfits based on the climate of your destination.

This is one of the most useful ChatGPT prompts I’ve stumbled across lately, and I know for sure it’s going to come in handy as a way to alleviate scenes similar to the start of Home Alone (I’m usually the one running up and down stairs, grabbing things last minute).

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Denodo Announces Plans to Further Support AI Innovation by Releasing Denodo DeepQuery, a Deep Research Capability — TradingView News

Published

3 hours ago

July 7, 2025

The Editors

PALO ALTO, Calif., July 07, 2025 (GLOBE NEWSWIRE) — Denodo, a leader in data management, announced the availability of the Denodo DeepQuery capability, now as a private preview, and generally available soon, enabling generative AI (GenAI) to go beyond retrieving facts to investigating, synthesizing, and explaining its reasoning. Denodo also announced the availability of Model Context Protocol (MCP) support as part of the Denodo AI SDK.

Built to address complex, open-ended business questions, DeepQuery will leverage live access to a wide spectrum of governed enterprise data across systems, departments, and formats. Unlike traditional GenAI solutions, which rephrase existing content, DeepQuery, a deep research capability, will analyze complex, open questions and search across multiple systems and sources to deliver well-structured, explainable answers rooted in real-time information. To help users operate this new capability to better understand complex current events and situations, DeepQuery will also leverage external data sources to extend and enrich enterprise data with publicly available data, external applications, and data from trading partners.

DeepQuery, beyond what’s possible using traditional generative AI (GenAI) chat or retrieval augmented generation (RAG), will enable users to ask complex, cross-functional questions that would typically take analysts days to answer—questions like, “Why did fund outflows spike last quarter?” or “What’s driving changes in customer retention across regions?” Rather than piecing together reports and data exports, DeepQuery will connect to live, governed data across different systems, apply expert-level reasoning, and deliver answers in minutes.

Slated to be packaged with the Denodo AI SDK, which streamlines AI application development with pre-built APIs, DeepQuery is being developed as a fully extensible component of the Denodo Platform, enabling developers and AI teams to build, experiment with, and integrate deep research capabilities into their own agents, copilots, or domain-specific applications.

“With DeepQuery, Denodo is demonstrating forward-thinking in advancing the capabilities of AI,” said Stewart Bond, Research VP, Data Intelligence and Integration Software at IDC. “DeepQuery, driven by deep research advances, will deliver more accurate AI responses that will also be fully explainable.”

Large language models (LLMs), business intelligence tools, and other applications are beginning to offer deep research capabilities based on public Web data; pre-indexed, data-lakehouse-specific data; or document-based retrieval, but only Denodo is developing deep research capabilities, in the form of DeepQuery, that are grounded in enterprise data across all systems, data that is delivered in real-time, structured, and governed. These capabilities are enabled by the Denodo Platform’s logical approach to data management, supported by a strong data virtualization foundation.

Denodo DeepQuery is currently available in a private preview mode. Denodo is inviting select organizations to join its AI Accelerator Program, which offers early access to DeepQuery capabilities, as well as the opportunity to collaborate with our product team to shape the future of enterprise GenAI.

“As a Denodo partner, we’re always looking for ways to provide our clients with a competitive edge,” said Nagaraj Sastry, Senior Vice President, Data and Analytics at Encora. “Denodo DeepQuery gives us exactly that. Its ability to leverage real-time, governed enterprise data for deep, contextualized insights sets it apart. This means we can help our customers move beyond general AI queries to truly intelligent analysis, empowering them to make faster, more informed decisions and accelerating their AI journey.”

Denodo also announced support of Model Context Protocol (MCP), and an MCP Server implementation is now included in the latest version of the Denodo AI SDK. As a result, all AI agents and apps based on the Denodo AI SDK can be integrated with any MCP-compliant client, providing customers with a trusted data foundation for their agentic AI ecosystems based on open standards.

“AI’s true potential in the enterprise lies not just in generating responses, but in understanding the full context behind them,” said Angel Viña, CEO and Founder of Denodo. “With DeepQuery, we’re unlocking that potential by combining generative AI with real-time, governed access to the entire corporate data ecosystem, no matter where that data resides. Unlike siloed solutions tied to a single store, DeepQuery leverages enriched, unified semantics across distributed sources, allowing AI to reason, explain, and act on data with unprecedented depth and accuracy.”

Additional Information

Denodo Platform: What’s New
Blog Post: Smarter AI Starts Here: Why DeepQuery Is the Next Step in GenAI Maturity
Demo: Watch a short video of this capability in action.

About Denodo

Denodo is a leader in data management. The award-winning Denodo Platform is the leading logical data management platform for transforming data into trustworthy insights and outcomes for all data-related initiatives across the enterprise, including AI and self-service. Denodo’s customers in all industries all over the world have delivered trusted AI-ready and business-ready data in a third of the time and with 10x better performance than with lakehouses and other mainstream data platforms alone. For more information, visit denodo.com.

Media Contacts

pr@denodo.com

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