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You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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Finished “We Begin at the End” by Chris Whitaker. A bit whodunit, a bit about coping with tragedy, and a bit about holding onto a past that can never be again.
Duchess Day Radley is a young girl who had to grow up fast. She raises her young brother while her mom suffers with substance abuse and a violent man. A self proclaimed outlaw, Duchess lashes out at the world rebelliously. But when tragedy strikes, so too does trouble.
4/5 stars ⭐️⭐️⭐️⭐️

We Begin at the End
Right. Wrong. Life is lived somewhere in between. Duch…

“The United States of America came within a few minutes of dying at the Battle of Gettysburg.
In fact, America came within seconds of being destroyed.
During those decisive moments a unit of the United States Army saved the republic.
The saviors of the Union were from Minnesota.”
https://stev…

Thank you, Minnesota
There is no greater obligation for an American than loyalty to the Constitution of the United States.

🇺🇦 #NowPlaying on KEXP's #Audioasis
Shaprece:
🎵 How Are You
#Shaprece
https://open.spotify.com/track/66mMLmbSPaQYET07DXNJLu

"Without us, right now you'd all be speaking German".
Es ist der Verdienst von #Trump, dass niemand in der #Schweiz Deutsch spricht.

»Der Begriff ›Halluzinationen‹ hat sich durchgesetzt, aber psychologisch ist das etwas ganz anderes. Da geht es um Sinneseindrücke, die in Wirklichkeit kein reales Gegenbild haben. Deswegen sollte man bei Sprachmodellen eigentlich von Konfabulationen sprechen. Es gibt nämlich eben auch Patienten, die tatsächlich nur Wörter aneinanderhäkeln. Und das nennt man Konfabulation, aber wahrscheinlich ist der Zug abgefahren.«
Quelle:

Wird künstliche Intelligenz uns überflügeln, Katharina Zweig?
KI könnte Jobs überflüssig machen und Menschen dümmer. Übernimmt sie gar die Weltherrschaft? Da sind Zweifel angebracht, meint die Sozioinformatikerin Katharina Zweig im Interview.

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