Tweet Teratas untuk #patch_seq

8 bulan yang lalu

100% agreed. Some domains of biology demand decades of embodied expertise. For example, one of my core expertise is to carry out #Patch_clamp electrophysiology and #Patch_seq for super difficult samples in vivo. I spend years in my PhD to acquire this skill! Patch-clamp electrophysiology, for example, isn’t just “running an instrument” — it’s micromanipulation, tissue intuition, signal interpretation, troubleshooting noise in real time, and adapting protocols on the fly. That kind of tacit knowledge isn’t easily automated. It's not just like pressing a button and asking neurons to get "patch" by themselves kind of magic :) Secondly, importantly, biology still contains vast unknowns. We don’t fully understand how organelles coordinate dynamically, how #bioelectric states regulate cell fate, or how subtle modes of cell–cell communication emerge under stress or disease. Many of these phenomena aren’t even properly measured yet. AI is powerful at pattern recognition within existing data distributions. But it cannot infer ground truth where none exists. If the phenomenon hasn’t been captured, quantified, or instrumented, AI has nothing to learn from. The future isn’t AI replacing biology. It’s AI amplifying biologists. once we generate deeper, higher-quality measurements of the unknown.

274

thmonk @thmonk_pf

hampir 3 tahun yang lalu

Patch-seqによるヒトGABAergic neuronの分類/特徴 Science特集発現型分類は形態/機能dataにて構築/更新すべきヒトはRatと比較し・樹状構造は分枝が少なく空間的に広がっている・興奮性が高くAP発射閾値が低い・一瞬のAPの半値幅は大きい #patch_seq #GABA #papers https://t.co/7CChPy0P1p

Tweet Teratas untuk #patch_seq

Tagar Terakhir Terlihat di Sotwe

Tren untuk Anda

Pengguna Paling Populer