Kevin K. Yang 楊凱筌

@KevinKaichuang · Massachusetts, USA · joined 07 Nov 2016

Principal Researcher in BioML @MSFTResearch (@MSRNE). He/him/他. 🇹🇼

24 111Followers

6 381Following

11 795Posts total

24.3KViews on collected posts

Neueste Beiträge

Kevin K. Yang 楊凱筌@KevinKaichuang17 Sep

@KevinKaichuang what do you think is the role of a bioinformatician now that for everything data-science related we have gpt 6, fable 5.1, claude science, and etc? 33 views · 0 likes · 0 reposts · 0 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang17 Sep

@KevinKaichuang Not shocking, proposing doing calculus at work will get me shot when your colleague proposes a simpler heuristic using a basic formula 84 views · 1 likes · 0 reposts · 0 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang16 Sep

It's actually shocking I got as far as I did without taking linear algebra at all in undergrad 11.5K views · 77 likes · 1 reposts · 3 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang16 Sep

FoldDir: A flow-matching structure-conditioned protein sequence design model with excellent computational results and some functional nanobody redesigns. @AlpTartici @MichaelCJewett @Rbaltman https://t.co/Vaew7Oe66T

3.7K views · 67 likes · 9 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang14 Sep

A nice review on ways to discover and engineer proteins that use fewer amino acids while maintaining structure and function. @FilipBuchel @KlaraH_lab https://t.co/9c2aHiI10B

5.6K views · 97 likes · 16 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang11 Sep

A tractable way to incorporate pairwise interactions in ancestral sequence reconstruction. https://t.co/aq3O6VWTR8

3.4K views · 53 likes · 11 reposts · 1 replies Open on X →

Kevin K. Yang 楊凱筌@KevinKaichuang17 May 2019

We (@ZvxyWu, @francesarnold) wrote a review about machine learning for protein engineering, but there's so many new papers that we can't update it quickly enough. So Zach and I made a public list of papers covering machine learning on proteins. https://t.co/KYKhcDvv2h 0 views · 747 likes · 186 reposts · 23 replies Open on X →

Im Vergleich zu Konten gleicher Größe

6 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird mehr Menschen gezeigt als Konten gleicher Größe.

Medianaufrufe3 554dieses Konto924Median für 10K–100K

Reichweite, %14.74%dieses Konto3.62%Median für 10K–100K

Interaktion, %1.55%dieses Konto1.52%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	3 554	924	3.85×
Reichweite (Aufrufe ÷ Follower)	14.74%	3.62%	4.07×
Interaktionsrate	1.55%	1.52%	1.02×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

3.4K11 Sep

5.6K14 Sep

3.7K16 Sep

11.5K

8417 Sep

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.92%11 Sep

2.03%14 Sep

2.07%16 Sep

0.71%

1.19%17 Sep

0.00%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes56.2%295 in total

Reposts7.0%37 in total

Replies1.1%6 in total

Quotes0.2%1 in total

Bookmarks35.4%186 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.