AI Applications on Stephanie Rebecca

Predictive Analysis in Biotech

Sat, 11 Jan 2025 00:00:00 +0000

Evaluating the mechanism of action (MOA) of therapeutic targets, specifically differentiating between upstream and downstream interventions. Systematically analysing how the position of a target within a biological pathway influences the probability of success (POS). For example, if a biotech company targets RAS mutations in pancreatic cancer but the primary oncogenic drivers occur downstream of RAS in the signalling cascade, the target is less likely to succeed in trials. Upstream targets, often involved in early signalling processes, present unique challenges and opportunities compared to downstream targets, which tend to be closer to the therapeutic outcome. Understanding these distinctions allows for a more accurate prediction of clinical trial outcomes, regulatory approval likelihood, and eventual market performance. Let’s dive into how predictive analysis reshapes biotech equities, delve into the intricacies of upstream versus downstream MOAs, and discuss how this innovative approach reshapes the investment landscape in the biotechnology sector.

Factors Influencing Probability of Success (POS)

Target Biology and MOA

Understanding the biological context of a therapeutic target is foundational. Upstream targets, like transcription factors or signalling regulators, can disrupt entire pathways but often face redundancy due to compensatory mechanisms. These targets frequently suffer from poor druggability due to structural challenges or lack of surface binding pockets. Conversely, downstream targets—closer to the disease phenotype—are more actionable, offering measurable biomarkers but posing risks of off-target effects due to their proximity to cellular machinery critical for normal functions.

Differential Privacy application to Federated Learning

Thu, 03 Oct 2024 00:00:00 +0000

I want to talk about ***a rabbit hole I have fallen down *since reading a paper on The Promises and Predicaments of Federated Learning in Healthcare. **

Last year, I had the privilege of working with an incredible team focused on applying machine learning techniques to tackle data interoperability challenges within our healthcare system. We grappled with issues like disparate data formats, strict privacy regulations, and the sheer volume of sensitive patient information scattered across multiple institutions. These hurdles sparked my curiosity about how emerging technologies could offer solutions, ultimately leading me to delve deeper into federated learning and privacy-enhancing technologies. These challenges led me to further explore the promise of technologies, such as homomorphic encryption and differential privacy, and their integration into federated learning.

AI Impact Scenario Exploration

Wed, 25 Sep 2024 00:00:00 +0000

Attachments

Scenario Impact of AI.pdf

Can LLMs Generate Novel Research Ideas

Tue, 24 Sep 2024 00:00:00 +0000

Can large language models (LLMs) actually produce novel, expert-level research ideas? In the grand tapestry of progress we are witnessing LLMs like GPT-o1 demonstrating remarkable capabilities in knowledge and reasoning. Solving challenging mathematical problems, assisting scientists in writing proofs, retrieving related works, generating code, and discovering patterns. These feats hint at a future where AI doesn’t just follow human instructions but contributes creatively to human endeavours.

The Promise and the Question A growing number of researchers propose autonomous agents that can generate and validate new ideas independently.

TALOS.AI

Tue, 24 Sep 2024 00:00:00 +0000

Attachments

TALOS pitch deck.pdf

TypeScript AI Paradigm

Fri, 20 Sep 2024 00:00:00 +0000

TypeScript’s momentum is unmistakable. Conversations with engineers and insights from the TypeScript Congress 2023 highlight its growing prominence. JetBrains’ Developer Survey shows TypeScript’s user share has tripled from 12% in 2017 to 34% in 2022 and 2023.

TypeScript: Scaling JavaScript for the Enterprise

As a superset of JavaScript, TypeScript extends the language by adding static typing. This enhancement allows developers to explicitly define data types, embedding context directly into the code. The result is “JavaScript that scales,” facilitating quicker onboarding, improved team collaboration, and earlier bug detection. Beyond scalability, TypeScript’s computational capabilities make it increasingly vital for AI applications, especially as more developers integrate Large Language Models (LLMs) into their projects.