Bioinformatics

Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

1. Alain Pulfer
2. Diego Ulisse Pizzagalli
3. Paolo Armando Gagliardi
4. Lucien Hinderling
5. Paul Lopez
6. Romaniya Zayats
7. Pau Carrillo-Barberà
8. Paola Antonello
9. Miguel Palomino-Segura
10. Benjamin Grädel
11. Mariaclaudia Nicolai
12. Alessandro Giusti
13. Marcus Thelen
14. Luca Maria Gambardella
15. Thomas T Murooka
16. Olivier Pertz
17. Rolf Krause
18. Santiago Fernandez Gonzalez

• Curated by eLife

  eLife assessment

  This valuable study advances our understanding of spatial-temporal cell dynamics both in vivo and in vitro. The authors provide solid evidence for their innovative deep learning-based apoptosis detection system, ADeS, which utilizes the principle of activity recognition. This work will be of broad interest to cell biologists and neuroscientists.

Reviewed by eLife

Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration

1. Mathias Fynbo Jensen
2. Morten Nielsen

• Curated by eLife

  eLife assessment

  This study presents a useful tool for predicting TCR specificity with compelling evidence for improvements over prior art. This work/tool will be broadly relevant to computational biologists and immunologists.

Reviewed by eLife

A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence

1. Kamil Pabis
2. Diogo Barardo
3. Olga Sirbu
4. Kumar Selvarajoo
5. Jan Gruber
6. Brian K Kennedy

• Curated by eLife

  eLife assessment

  This study presents fundamental findings on the role of transcription readout and intron retention in transposon expression during aging in mammals. The evidence supporting the claims of the authors is compelling, strongly supporting the authors' claims. The work will be of interest to scientists studying aging, transcription regulation, and epigenetics.

Reviewed by eLife

Deep Batch Active Learning for Drug Discovery

1. Michael Bailey
2. Saeed Moayedpour
3. Ruijiang Li
4. Alejandro Corrochano-Navarro
5. Alexander Kötter
6. Lorenzo Kogler-Anele
7. Saleh Riahi
8. Christoph Grebner
9. Gerhard Hessler
10. Hans Matter
11. Marc Bianciotto
12. Pablo Mas
13. Ziv Bar-Joseph
14. Sven Jager

• Curated by eLife

  eLife assessment

  This valuable study reports novel active learning batch selection methods that have been applied to optimization tasks related to ADMET and affinity properties relevant within the drug discovery field. While the evidence is solid, the paper could have benefited from a clearer and deeper description of methods as well as interpretation of the obtained models, and a wider comparison to existing methods. The article will be of general interest to scientist working in the field of drug discovery and, in general, to researchers within the fields of machine learning and data analysis.

Reviewed by eLife

Classification of bioactive peptides: a comparative analysis of models and encodings

1. Edoardo Bizzotto
2. Guido Zampieri
3. Laura Treu
4. Pasquale Filannino
5. Raffaella Di Cagno
6. Stefano Campanaro

Reviewed by Arcadia Science

A benchmark for large language models in bioinformatics

1. Varuni Sarwal
2. Gaia Andreoletti
3. Viorel Munteanu
4. Ariel Suhodolschi
5. Dumitru Ciorba
6. Viorel Bostan
7. Mihai Dimian
8. Eleazar Eskin
9. Wei Wang
10. Serghei Mangul

Reviewed by Arcadia Science

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis

1. Dimitar Georgiev
2. Simon Vilms Pedersen
3. Ruoxiao Xie
4. Álvaro Fernández-Galiana
5. Molly M. Stevens
6. Mauricio Barahona

Reviewed by Arcadia Science

Functional annotation hypothetical proteins: a world to be explored in drug development in Trypanosomatids

1. Raissa Santos de Lima
2. Ana Carolina Silva Bulla
3. Manuela Leal da Silva

Reviewed by Arcadia Science

Variance Analysis of LC-MS Experimental Factors and Their Impact on Machine Learning

1. Tobias Greisager Rehfeldt
2. Konrad Krawczyk
3. Simon Gregersen Echers
4. Paolo Marcatili
5. Pawel Palczynski
6. Richard Röttger
7. Veit Schwämmle

Reviewed by GigaScience

When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes

1. Bianca-Maria Cosma
2. Ramin Shirali Hossein Zade
3. Erin Noel Jordan
4. Paul van Lent
5. Chengyao Peng
6. Stephanie Pillay
7. Thomas Abeel

Reviewed by GigaScience