<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Week 7 HW: Genetic Circuits Part II :: 2026a-abhinav-rajendran</title><link>https://pages.htgaa.org/2026a/abhinav-rajendran/homework/week-07-hw-genetic-circuits-part-ii/index.html</link><description>Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) 1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Boolean circuits force biological signals into binary (high/low), but real biomarkers exist at continuous concentrations. IANNs operate on analog values, performing weighted summation and nonlinear activation (ReLU), so they can compute complex continuous functions like bandpass filters and diagonal decision boundaries. These are the kinds of input-output shapes actually needed for problems like cancer classification, where you care about relative concentration levels, not just on/off.</description><generator>Hugo</generator><language>en</language><atom:link href="https://pages.htgaa.org/2026a/abhinav-rajendran/homework/week-07-hw-genetic-circuits-part-ii/index.xml" rel="self" type="application/rss+xml"/></channel></rss>