Life Sciences

This study developed a graph-based QSAR modeling pipeline integrating assay data preprocessing, fingerprint and molecular graph feature representations, and benchmarking of classical machine learning, graph neural networks, graph transformers, and their consensus ensembles, applied to predict Caspase-3/7 activation, mitochondrial membrane potential disruption, and FDA drug-induced liver injury, where Graphormer achieved the highest F1 of 0.79 and the full consensus model achieved the highest AUC of 0.69 on DILI prediction, surpassing the previous best model with AUC 0.63 and F1 0.65, and identified structural motifs associated with dual activation and cell-line-specific responses through fragment enrichment analysis.

Using DEFT, an LLM-guided interpretable decision-tree framework, the authors found that pause sites are discriminated by a G at the pause position, C or T at +1 and a minimum G content in the 49 upstream bases (held-out AUROC 0.92, accuracy 0.87); inserting a 377 bp G-less cassette at the 5' ends of NDRG1 and HSP90AA1 abolished the promoter-proximal pause in an orientation-dependent way without impairing hypoxic or heat-shock activation (NDRG1 was even super-activated), while ChIP 5' RNAPII, Ser5P and NELF peaks persisted without NET-seq-detectable pausing and CTD deletion weakened the pause and shifted its peak from ~+80 to ~+40, supporting a model in which the pause is initiated by G-dependent arrest and stabilized by factors including CTD-mediated tethering.

Using GISTIC copy number from the TCGA PanCancer Atlas to classify tumors as PTEN intact, hemizygous, or homozygous deletion, this study scored a five-gene glutaminolysis signature (GLS, SLC1A5, GOT1, GLUD1, GPT2) against loss severity across fourteen tumor types and found that the signature decreased with PTEN loss in all fourteen (significantly in twelve) but was not MYC-mediated; instead the decline tracked chromosomal position, since GLUD1 and GOT1 flank PTEN on 10q and thirty-seven neighboring genes carrying no glutaminolysis annotation tracked PTEN copy number just as closely (mean rho 0.843 versus 0.842), with co-deletion fidelity falling monotonically with distance from PTEN (rho = -0.

The study presents AlloPool, a graph neural network framework that combines temporal attention with iterative edge pooling to learn minimal, time-evolving residue interaction networks from equilibrium and non-equilibrium molecular dynamics trajectories, reconstructing trajectories at sub-angstrom RMSD across Pin1, the GAIN mechanosensor domain, dopamine D2 and beta-1 adrenergic receptors, the SdrG adhesin, PDZ3, and the Engrailed homeodomain, and using those networks to map allosteric pathways, predict ligand pharmacology, mechanical loading states, and mutation effects.

In 1,095 carriers of pathogenic or likely pathogenic sarcomere variants across three international centers, a previously validated AI-ECG model applied to 12-lead ECG images yielded an AUROC of 0.91 for the HCM phenotype at baseline and 0.92 for manifest HCM, a higher AI-ECG score among genotype-positive/phenotype-negative individuals predicted incident HCM during follow-up (unadjusted HR 1.55 per 1-SD; adjusted HR 1.38), and in 57,007 UK Biobank participants the AI-ECG score and a polygenic risk score were independent and additive, with adjusted odds of HCM of 60.2 when both were high.

EvSpark is a speculative decoding system for hybrid convolutional-recurrent-attention DNA foundation models such as Evo2: it verifies draft blocks in parallel and restores all three classes of inference state by selecting retained intermediate states without replay, reaching 2.96x on 43 real-sequence prompts and 3.27x including five synthetic controls on an Evo2 7B 48-prompt benchmark with three training seeds, retaining 1.84x-2.43x at 262k context, and yielding 2.18x-2.46x on real sequences and 2.51x-2.78x on the full suite for 20B and 40B targets.

This study introduces and evaluates a privacy-preserving knowledge distillation framework in which CTGAN-generated synthetic cohorts matching UK Biobank distributions are used to elicit multimorbidity scores from three teacher LLMs (GPT-4o, Gemini, DeepSeek) under zero-shot prompting, and compact student models (CoLLMs) are then trained to mimic those scores, enabling application to real UK Biobank data (N = 439,221) for multimorbidity scoring without exposing patient-level data to third-party APIs, with evaluation against the Charlson (CCI) and Elixhauser (ECI) indices via survival analysis, genome-wide association studies, and polygenic risk score associations.