Bias in resistance gene prediction due to repeat masking
P. Bayer, D. Edwards, J. Batley
semanticscholar +1 more source
Proteomic and phosphoproteomic analyses were performed on lung adenocarcinoma (LUAD) tumors with EGFR, KRAS, or EML4–ALK alterations and wild‐type cases. Distinct protein expression and phosphorylation patterns were identified, especially in EGFR‐mutated tumors. Key altered pathways included vesicle transport and RNA splicing.
Fanni Bugyi+12 more
wiley +1 more source
Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities. [PDF]
Zhang H+6 more
europepmc +1 more source
ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture. [PDF]
Kushwaha SK, Åhman I, Bengtsson T.
europepmc +1 more source
Digital filters for gene prediction applications [PDF]
P. P. Vaidyanathan, Byung-Jun Yoon
openalex +1 more source
Targeting of PTP4A3 overexpression sensitises HGSOC cells towards chemotherapeutic drugs
In HGSOC with normal KRAS expression, high PTP4A3 expression regulates autophagy activation. Conversely, in HGSOC with high KRAS expression, KRAS dictates autophagy control, and PTP4A3 is not required. When high PTP4A3 expression is inhibited, HGSOC cells are preferentially sensitised towards DNA‐damaging agents.
Ana López‐Garza+3 more
wiley +1 more source
Pericytes change function depending on glioblastoma vicinity: emphasis on immune regulation
Pericytes alter their transcriptome depending on their proximity to the tumor core. In the tumor core, pericytes display a more active state with higher communication strength but with lower immune activation potential and a shift toward extracellular matrix production.
Carolina Buizza+5 more
wiley +1 more source
BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows. [PDF]
Strepis N+14 more
europepmc +1 more source
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example. [PDF]
Hatoum AS+8 more
europepmc +1 more source
A Bayesian framework for combining gene predictions [PDF]
Vladimir Pavlović+2 more
openalex +1 more source