BIOS | · 9 min read By: Alix Ventures — Supporting Early Stage Life Science Startups Driving Patient Impact Intro It’s no secret that the traditional drug discovery model is expensive, time-consuming, and has recently produced compounds with marginal efficacy in clinical trials and high failure rates. Large biopharma companies are increasingly outsourcing R&D to academic labs and small/mid-size startups via licensing partnerships […]
26 Mar 2021 | Source According to a recent study, machine learning algorithms are expected to replace 25% of the jobs across the world, in the next 10 years. With the rapid growth of big data and availability of programming tools like Python and R –machine learning is gaining mainstream presence for data scientists. Machine learning applications […]
STANFORD ENGINEERING STAFF November 10, 2020 Daphne Koller, a veteran of AI, explains why she left academia for a chance to change the pharmaceutical industry. STOCKSY/SERGIO MARCOS How can AI influence medicine and biology? In a world where a drug takes years and billions of dollars to develop, just one in 20 candidates makes it to […]
Check out this video on how Machine Learning / Artificial Intelligence combined with RPA can help scale automation across different departments in their organization.
Author: Edward Huskin Implementing a neural network from scratch is a deeply valuable exercise, especially for someone just picking up on the fundamentals of machine learning. Once all the concepts have sunk in, writing a library that does something as common as image classification, and OCR is basically attempting to reinvent the wheel. Rather than […]
While healthcare companies don’t seem to be publicizing their efforts into AI for R&D efforts this article details the current AI initiatives at 3 of the largest in the world. Such initiatives include, optimization of numerous processes within their business, claims management, reimbursement, and data management.
Author: Savaram Ravindra Introduction The machine learning systems have been around since the 50s, but there are three major factors at play. They are improved algorithms, more powerful computer hardware, and the enormous increase of data. This is the reason why machine learning has gained a lot of significance than it had in the 50s. […]
See how Mount Sinai is developing large-scale image data in neuropathology for building and evaluating deep learning algorithms to diagnose diseases.
Check out this informative guide on deep learning.
Learn how a software engineer transitioned into machine learning through a data science bootcamp.