This blog recommends criteria for enterprises to evaluate cloud connectors and ensure the benefits outweigh the costs.
Published at:
https://www.eckerson.com/articles/evaluating-cloud-connectors-for-the-customer-360-data-program

Evaluating Cloud Connectors For The Customer 360 Data Program - Audio Blog

Providing analysts with tools that increase their efficiency is critical to helping them do more work with less effort.
Published at:
https://www.eckerson.com/articles/a-data-analyst-s-guide-to-the-data-catalog

A Data Analyst’s Guide To The Data Catalog - Audio Blog

Listen to the press release about a new research paper published in Volume 13 of Oncotarget, “Predicting cancer immunotherapy response from gut microbiomes using machine learning models.”
___________________________________

A new research paper was published in Oncotarget on July 19, 2022, entitled, “Predicting cancer immunotherapy response from gut microbiomes using machine learning models.”

“In the last decade, the use of cancer immunotherapy targeting immune checkpoint inhibitors (ICIs) to boost T cell mediated cancer cell clearance has significantly improved cancer patient survival [1].”

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. 

In this new study, by Hai Liang, Jay-Hyun Jo, Zhiwei Zhang, Margaret A. MacGibeny, Jungmin Han, Diana M. Proctor, Monica E. Taylor, You Che, Paul Juneau, Andrea B. Apolo, John A. McCulloch, Diwakar Davar, Hassane M. Zarour, Amiran K. Dzutsev, Isaac Brownell, Giorgio Trinchieri, James L. Gulley, and Heidi H. Kong from the National Institutes of Health Library, National Cancer Institute, National Human Genome Research Institute, West Virginia University, Zimmerman Associates Inc., and the University of Pittsburgh, researchers used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. 

Full press release - https://www.oncotarget.com/news/pr/oncotarget-predicting-cancer-immunotherapy-response-from-gut-microbiomes-using-machine-learning-models/

DOI - https://doi.org/10.18632/oncotarget.28252

Correspondence to - Heidi H. Kong - konghe@mail.nih.gov

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Keywords - gut microbiome, immunotherapy, 16S rRNA, machine learning, metagenomics

About Oncotarget

Oncotarget is a peer-reviewed, open access biomedical journal covering research on all aspects of oncology.

To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us:

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Oncotarget is published by Impact Journals, LLC: https://www.ImpactJournals.com

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Oncotarget

Predicting Cancer Immunotherapy Response From Gut Microbiomes Using Machine Learning Models

Professor Andrew Su is based in the Department of Integrative Structural and Computational Biology, where his lab develops digital tools to draw conclusions from the vast streams of medical data generated each day. Listen to us discuss gaming as a way of being a scientist, the replacement of doctors with machine-learning and your Netflix watching history. 

Show notes:

Episode transcript: https://www.scripps.edu/_files/images/2022_images/podcast-images/Andrew-Su-transcript.pdf

Andrew’s TEDx talk: Building the knowledgebase of biology using citizen science
https://www.youtube.com/watch?v=hT_pj1cB0Q8
 
Su lab website
https://sulab.org/ 

Tweet: @andrewsu

Citizen science initiatives:
https://www.zooniverse.org/ 
https://fold.it/ 
https://stallcatchers.com/main 

Explore the Scripps Research Magazine
https://magazine.scripps.edu/ 

Subscribe to receive our monthly e-newsletter
https://signup.e2ma.net/signup/1879047/1890109/ 

@scrippsresearch on Twitter

Episode 35 – Andrew Su: How artificial and community intelligence are shaping medicine

Fozzie - CYBERINSTS

Decision AI is an emerging discipline that enables faster, smarter operational decisions by combining decision intelligence and contextual intelligence.
Published at:
https://www.eckerson.com/articles/decision-ai-and-the-opportunity-for-smarter-faster-actions

Decision AI And The Opportunity For Smarter, Faster Actions - Audio Blog

For the most innovative and forward-thinking organisations, the next frontier forward for data is focused on machine learning, and specifically the role that MLOps plays in driving outcomes.
Questions that data leaders need to be asking themselves now include: What steps do organisations need to take to deliver ML maturity, how can they take the leap from experimentation to production, and how ML teams be effectively organised and motivated around this goal?

To dive deeply into this critical discussion, Data Futurology recently brought together a panel of some of the leaders in ML strategy and execution. Each of these companies have been successful in productionalising ML across their enterprises, and discuss their strategies and successes in an open and free-flowing discussion:

Agustinus Nalwan, Head of AI and Machine Learning, carsales.com.au
Farhan Baluch, Principal Data Scientist, Apple (USA)
Kendra Vant, Executive GM Data, ML & AI, Xero
Ram Radhakrishnan, General Manager Customer Analytics, AI & Data Science at Woolworths Group

These four experts also highlight just how important it is to motivate teams around an ongoing process of learning and discuss how to deliver a dynamic understanding of the changing role of data across the organisation. Whether the data team is inwardly-looking, or focused on customer outcomes, emerging concepts such as “software 2.0” – as mentioned in the webinar – will continue to throw curveballs that MLOps teams will need to have the agility to adapt to and capitalise on.

Ahead of the Scaling AI with MLOps event to be held in Melbourne on October 25, this webinar is a unique opportunity to gain insight from those at the very bleeding edge of data innovation.

Enjoy the show!

Thank you to you our sponsor, Talent Insights Group!
Join us for one of our upcoming events: https://www.datafuturology.com/events
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#199 Productionalising ML Across The Enterprise - What It Takes To Get This Right Audio V2

A new research paper was published in Aging (abbreviated as "Aging (Albany NY)" by Medline/PubMed and as "Aging-US" by Web of Science) on the cover of Volume 14, Issue 14, entitled, “Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer’s disease.”

Alzheimer’s disease (AD) risk increases exponentially with age and is associated with multiple molecular hallmarks of aging, one of which is epigenetic alterations. Epigenetic age predictors based on 5’ cytosine methylation (DNAm), or epigenetic clocks, have previously suggested that epigenetic age acceleration may occur in AD brain tissue. 

“Epigenetic clocks are promising tools for the quantification of biological aging, yet we hypothesize that investigation of brain aging in AD will be assisted by the development of brain-specific epigenetic clocks.” 

In this new study, researchers Kyra L. Thrush, David A. Bennett, Christopher Gaiteri, Steve Horvath, Christopher H. van Dyck, Albert T. Higgins-Chen, and Morgan E. Levine, from Yale University, Rush University Medical Center, University of California Los Angeles, VA Connecticut Healthcare System, and Altos Labs, hypothesized that a brain age methylation-based predictor could be developed with meaningful disease associations and broad multi-brain-region utility.

“To test this, we used DNAm capture to generate a PC-based epigenetic predictor of brain aging which we show to: (1) strongly reflect AD neuropathology and cognitive decline, and (2) track age across multiple brain regions.”

The team generated a novel age predictor, termed PCBrainAge, that was trained solely in cortical samples. This predictor utilizes a combination of principal components analysis and regularized regression, which reduces technical noise and greatly improves test-retest reliability.

“To characterize the scope of PCBrainAge’s utility, we generated DNAm data from multiple brain regions in a sample from the Religious Orders Study and Rush Memory and Aging Project.”

PCBrainAge captures meaningful heterogeneity of aging: Its acceleration demonstrates stronger associations with clinical AD dementia, pathologic AD, and APOE ε4 carrier status compared to extant epigenetic age predictors. It further does so across multiple cortical and subcortical regions. 

“Overall, PCBrainAge’s increased reliability and specificity makes it a particularly promising tool for investigating heterogeneity in brain aging, as well as epigenetic alterations underlying AD risk and resilience.”

DOI: https://doi.org/10.18632/aging.204196 

Corresponding Author: Albert T. Higgins-Chen - Email: a.higginschen@yale.edu 

Keywords: epigenetic clocks, unsupervised machine learning, brain, Alzheimer's disease, age acceleration

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About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

Please visit our website at https://www.Aging-US.com​​ and connect with us:

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Aging (Aging-US)

Multi-region Methylation Principal Component Based Clock in the Context of Alzheimer’s

Steven Loeb speaks with Dr. Jon Bloom, co-founder and CEO of Podimetrics, a care management company that is looking to eradicate diabetic foot ulcers.

Thank you to our sponsors: Advsr; a boutique M&A advisory firm. They wrote the book on startup M&A called "Magic Box Paradigm: A framework for startup acquisitions." Go to Amazon.com to get your copy. Also thanks to Stratpoint, an outsourced engineering firm and Scrubbed, an online bookkeeping firm. If you need affordable and quality engineering and bookkeeping, check them out. We highly recommend them!

Interview with Dr. Jon Bloom, co-founder and CEO of Podimetrics

This week, Mikiko Bazeley, Senior Software Engineer at Mailchimp joins the podcast to share her in-depth knowledge of MLOps: Machine Learning Operations. Tune in to hear her discuss what it entails, why it's so critical for the efficiency of any data science team, and the most important tools you need to master for career success in this field.

In this episode you will learn:
• What MLOps is [11:40]
• Mikiko’s role at Mailchimp and why MLOps is critical for the efficiency of any data science team [27:11]
• The three most important MLOps tools [32:15]
• The six most essential MLOps skills for data scientists [47:01]
• The key factors Mikiko looks when hiring engineers [1:07:31]
• Mikiko’s productivity tricks for balancing software engineering, content creation, and her athletic pursuits [1:13:20]

Additional materials: www.superdatascience.com/599

The Super Data Science Podcast: Machine Learning | AI | Success

SDS 599: MLOps: Machine Learning Operations

Oncotarget is a primarily oncology-focused, peer-reviewed, open access journal. Papers are published continuously within yearly volumes in their final and complete form and then quickly released to Pubmed.

The Super Data Science podcast with Jon Krohn brings you the latest and most important machine learning, artificial intelligence, and broader data-world topics from across both academia and industry. As the quantity of data on our planet doubles every couple of years and this trend is set to continue for decades to come, there's an unprecedented opportunity for you to make an enormous impact in your lifetime. 

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, and commercialization − everything you need to crush it with data science.

A traditional, twice-monthly, peer-reviewed, open-access journal covering all areas of research on aging and age-related diseases, including cancer and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome.

In June 2022, Web of Science (Clarivate Analytics) released their 2021 JCR Impact Factor List. Aging is pleased to report that our 2021 impact factor is 5.955. This number has increased from last year’s 5.682.

Listen to data and analytics leaders share the secrets of their success. Wayne Eckerson, long-time global thought leader interviews guests who run data and analytics programs at Fortune 2000 organizations around the world. Tune in to stay abreast of the latest technologies, techniques, and trends in our fast-paced industry.

This award-winning podcast brings Scripps Research discoveries to life, exploring the personal stories of the people behind the science on their quest to solve humanity’s biggest biomedical problems. 

Sit down with our host Drew Duglan, PhD, and find out firsthand from experts at the cutting-edge of the life sciences.

Follow us on social media for more updates:
@scrippsresearch on Twitter and Instagram
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Data Futurology is data from a human lens. 
In Data Futurology, experienced Data Science Leaders from around the world tell us their stories, challenges and the lessons learned throughout their career. 
We also ask them: 
- What makes a great data scientist? What skills are required?
- How to become a great data science leader?
- How should I grow and get the most out of my team?
- What is a good data strategy? and how do I best implement it?
- What are interesting applications of ML/AI that I should be considering in my industry? 
To find out more visit www.datafuturology.com

New tracks tagged #machinelearning