FDA General

Context-Aware Synthetic Promoter Design Using Neural Networks Enables Rewiring of Eukaryotic Transcriptional Networks

Gene regulation through promoter engineering is a cornerstone of synthetic biology, enabling precise control over transcriptional networks. However, experimental approaches remain labor-intensive. While artificial neural networks (ANNs) have improved regulatory element prediction, tools for promoter–transcription factor binding site (TFBS) recombination are still lacking. We present an ANN framewo...

Variant-resolved prediction of context-specific isoform variation with a graph-based attention model

In eukaryotes, most genes produce multiple transcript isoforms that diversify the transcriptome and proteome, serving as a key mechanism of functional regulation. Genetic variation can disrupt the RNA processing signals that shape isoform structure and abundance, yet modeling these effects at full-length isoform resolution remains challenging due to the complexity of transcript regulation. Here, w...

DLRNA-BERTa: A transformer approach for RNA-drug binding affinity prediction

RNA-based therapies are a rapidly expanding field, offering treatments for a wide range of diseases, including many rare conditions. To date, 24 RNA t...

Stable Maintenance of Two-Cell-Like Cells from Embryonic Stem Cells Reveals Chromatin and Super Enhancer Regulation of MERVL Elements

Mouse embryonic stem cells (ESCs) occasionally transit into a rare two-cell-like (2C) state characterized by transient activation of endogenous retrov...

Octopamine signaling from clock neurons plays dual roles in Drosophila long-term memory

Circadian clock genes are best known for regulating circadian rhythms, but they also play crucial roles in memory processes. This suggests that memory...

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of ac...

Accelerating Drug Discovery with HyperLab: An Easy-to-Use AI-Driven Platform

HyperLab, developed by HITS, is a web-based, AI-driven drug discovery platform designed to increase research efficiency for experimental drug discover...

Integrative Chemical Genetics Platform Identifies Condensate Modulators Linked to Neurological Disorders

Aberrant biomolecular condensates are implicated in multiple incurable neurological disorders, including Amyotrophic Lateral Sclerosis, Frontotemporal...

Hybrid deep learning–mechanistic modeling of cellular dynamics from a spatiotemporal single-cell atlas

Single-cell measurement technologies provide a powerful framework for studying cellular heterogeneity, transitions, and regulatory networks, yet recon...

A blueprint for mutation-defined hallmark vulnerabilities across human cancers

Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer depe...

DeviceAgent: An autonomous multimodal AI agent for flexible bioelectronics

The development of flexible bioelectronics remains a complex, multidisciplinary process that demands specialized expertise and labor-intensive efforts...

Targeted Enzymatic Fragmentation of Lipoprotein(a) via Kringle IV Domains: A Clearance-Enhancing Therapeutic Strategy for Cardiovascular Disease

Elevated lipoprotein(a) [Lp(a)] is an independent, genetically determined risk factor for atherosclerotic cardiovascular disease (ASCVD). Its unique a...

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment resistance and poor prognosis. Despite advances in ...

Gradual proactive regulation of body state by reinforcement learning of homeostasis

Living systems maintain physiological variables such as temperature, blood pressure, and glucose within narrow ranges—a process known as homeostasis. ...

CNValidatron: Accurate And Efficient Validation of PennCNV Calls Using Computer Vision

Large rare copy number variants (CNVs) are a main source of genetic variation in the genome and are important in both evolution and disease risk. CNVs...

Modelling transcription with explainable AI uncovers context-specific epigenetic gene regulation at promoters and gene bodies

Transcriptional regulation involves complex interactions with chromatin-associated proteins, but disentangling these mechanistically remains challengi...

Multivariate analysis of glycogenes reveals coordinated regulation of immunoglobulin glycosylation in an immortalized human B cell system

While neutralizing ability has traditionally been considered the most important antibody function, appreciation has grown for Fc-mediated ‘extra-neutr...

AXIS: A Lab-in-the-Loop Machine Learning approach for generalized detection of macromolecular crystals

Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robo...

A deep learning model captures position-specific effects of plant regulatory sequences and suggests genes under complex regulation

Deep neural networks can be trained to predict gene expression directly from genomic sequence, thereby implicitly learning regulatory sequence pattern...

From bench assays to bedside: a context-embedding transformer predicts monoclonal antibody viscosity, clearance, and regulatory success

Late-stage failures of monoclonal antibody (mAb) programs often reflect developability liabilities, including high-concentration viscosity and rapid c...