Why are graphs the future of biomedical research and what is the value of NLP here?

A small case study about:

How to speed up drug discovery with knowledge graphs and discover potential cures for diseases

In this case text mining is used to contextualize knowledge about:

- Genes
- Compounds
- Diseases
- Adverse drug effects
- Receptor bindings
Which text types are processed here? Medical literature, patient notes, electronic health records, clinical reports etc.

But how to start?

First you need to identify the different entities such as compounds, diseases, adverse drug effects and receptor bindings.
This is achieved through Natural Language Processing (NLP) and there are suitable pre-trained models for processing biomedical, scientific or clinical text like scispaCy

@spacy_io models for processing biomedical, scientific or clinical text
https://t.co/1EPFZCFwoc
Another library which is specialized in biomedical text is Spark NLP

@JohnSnowLabs

https://t.co/EYM8lIyuUp
The next challenge is to extract the different relations! Diseases are related to genes which are related to receptors and compounds can bind to these receptors.

Sounds simple at first but there are several problems that need to be solved
Problems to solve

1. Difficult to ingest and integrate complex networks of text mined outputs
2. Difficult to contextualize knowledge extracted from text with existing knowledge
3. Difficult to investigate insights in a scalable and efficient way
Fortunately, Grakn solves all our problems!

@GraknLabs

How it works is explained here: https://t.co/BCGDuWrVqA
To understand how NLP and graphs are used to link medical knowledge I recommend this talk on text mining and drug discovery at Novartis

Not quite up to date but aged very well

Connecting the Dots in Early Drug Discovery at Novartis
https://t.co/QNJt6q5IsU

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🌺श्री गरुड़ पुराण - संक्षिप्त वर्णन🌺

हिन्दु धर्म के 18 पुराणों में से एक गरुड़ पुराण का हिन्दु धर्म में बड़ा महत्व है। गरुड़ पुराण में मृत्यु के बाद सद्गती की व्याख्या मिलती है। इस पुराण के अधिष्ठातृ देव भगवान विष्णु हैं, इसलिए ये वैष्णव पुराण है।


गरुड़ पुराण के अनुसार हमारे कर्मों का फल हमें हमारे जीवन-काल में तो मिलता ही है परंतु मृत्यु के बाद भी अच्छे बुरे कार्यों का उनके अनुसार फल मिलता है। इस कारण इस पुराण में निहित ज्ञान को प्राप्त करने के लिए घर के किसी सदस्य की मृत्यु के बाद का समय निर्धारित किया गया है...

..ताकि उस समय हम जीवन-मरण से जुड़े सभी सत्य जान सकें और मृत्यु के कारण बिछडने वाले सदस्य का दुख कम हो सके।
गरुड़ पुराण में विष्णु की भक्ति व अवतारों का विस्तार से उसी प्रकार वर्णन मिलता है जिस प्रकार भगवत पुराण में।आरम्भ में मनु से सृष्टि की उत्पत्ति,ध्रुव चरित्र की कथा मिलती है।


तदुपरांत सुर्य व चंद्र ग्रहों के मंत्र, शिव-पार्वती मंत्र,इन्द्र सम्बंधित मंत्र,सरस्वती मंत्र और नौ शक्तियों के बारे में विस्तार से बताया गया है।
इस पुराण में उन्नीस हज़ार श्लोक बताए जाते हैं और इसे दो भागों में कहा जाता है।
प्रथम भाग में विष्णुभक्ति और पूजा विधियों का उल्लेख है।

मृत्यु के उपरांत गरुड़ पुराण के श्रवण का प्रावधान है ।
पुराण के द्वितीय भाग में 'प्रेतकल्प' का विस्तार से वर्णन और नरकों में जीव के पड़ने का वृत्तांत मिलता है। मरने के बाद मनुष्य की क्या गति होती है, उसका किस प्रकार की योनियों में जन्म होता है, प्रेत योनि से मुक्ति के उपाय...
How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)
The best morning routine?

Starts the night before.

9 evening habits that make all the difference:

1. Write down tomorrow's 3:3:3 plan

• 3 hours on your most important project
• 3 shorter tasks
• 3 maintenance activities

Defining a "productive day" is crucial.

Or else you'll never be at peace (even with excellent output).

Learn more


2. End the workday with a shutdown ritual

Create a short shutdown ritual (hat-tip to Cal Newport). Close your laptop, plug in the charger, spend 2 minutes tidying your desk. Then say, "shutdown."

Separating your life and work is key.

3. Journal 1 beautiful life moment

Delicious tacos, presentation you crushed, a moment of inner peace. Write it down.

Gratitude programs a mindset of abundance.

4. Lay out clothes

Get exercise clothes ready for tomorrow. Upon waking up, jump rope for 2 mins. It will activate your mind + body.

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Oh my Goodness!!!

I might have a panic attack due to excitement!!

Read this thread to the end...I just had an epiphany and my mind is blown. Actually, more than blown. More like OBLITERATED! This is the thing! This is the thing that will blow the entire thing out of the water!


Has this man been concealing his true identity?

Is this man a supposed 'dead' Seal Team Six soldier?

Witness protection to be kept safe until the right moment when all will be revealed?!

Who ELSE is alive that may have faked their death/gone into witness protection?


Were "golden tickets" inside the envelopes??


Are these "golden tickets" going to lead to their ultimate undoing?

Review crumbs on the board re: 'gold'.


#SEALTeam6 Trump re-tweeted this.