Genetics

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for improving patient outcomes. Here, we compare the performance of two machine learning models - FMCIB+XGBoost, a supervised model with domain-specific pretraining, and Dinov2+ABMIL, a self-supervised model with attention-based multiple-instance learning...

Hybrid High-Order formulations with turbulence modelling capabilities for incompressible flow problems

We propose a Hybrid High-Order (HHO) formulation of the incompressible Navier--Stokes equations, that is well suited to be employed for the simulation of turbulent flows. The spatial discretization relies on hybrid velocity and pressure spaces and the temporal discretization is based on Explicit Singly Diagonal Implicit Runge-Kutta (ESDIRK) methods. The formulation possesses some attractive feat...

Differentiable Generalized Sliced Wasserstein Plans

Optimal Transport (OT) has attracted significant interest in the machine learning community, not only for its ability to define meaningful distances...

Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. ...

Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. ...

LGLoc as a new language model-driven graph neural network for mRNA localization.

The localization of mRNA is crucial for the synthesis of functional proteins and plays a significant role in cellular processes. Understanding mRNA lo...

Robotic radiation shielding system reduces radiation-induced DNA damage in operators performing electrophysiological procedures.

Fluoroscopically guided electrophysiology (EP) procedures expose operators to low doses of ionizing radiation, which can induce DNA double-strand brea...

ID-Align: RoPE-Conscious Position Remapping for Dynamic High-Resolution Adaptation in Vision-Language Models

Currently, a prevalent approach for enhancing Vision-Language Models (VLMs) performance is to encode both the high-resolution version and the thumbn...

HAD: Hybrid Architecture Distillation Outperforms Teacher in Genomic Sequence Modeling

Inspired by the great success of Masked Language Modeling (MLM) in the natural language domain, the paradigm of self-supervised pre-training and fin...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. ...

Temporally discordant chromatin accessibility and DNA demethylation define short- and long-term enhancer regulation during cell fate specification.

Chromatin and DNA modifications mediate the transcriptional activity of lineage-specifying enhancers, but recent work challenges the dogma that joint ...

Ctrl-DNA: Controllable Cell-Type-Specific Regulatory DNA Design via Constrained RL

Designing regulatory DNA sequences that achieve precise cell-type-specific gene expression is crucial for advancements in synthetic biology, gene th...

Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data

In this study, we propose a machine learning-based method for noise reduction and disease-causing gene feature extraction in gene sequencing DeepSeq...

Toward Scientific Reasoning in LLMs: Training from Expert Discussions via Reinforcement Learning

We investigate how to teach large language models (LLMs) to perform scientific reasoning by leveraging expert discussions as a learning signal. Focu...

Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC), a cancer characterized by substantial immune infiltration, exhibits limited sensitivity to conventional radi...

Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA ...

Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data.

Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine lear...

Language Models Surface the Unwritten Code of Science and Society

This paper calls on the research community not only to investigate how human biases are inherited by large language models (LLMs) but also to explor...

Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation

Diffusion models have achieved state-of-the-art performance in generating images, audio, and video, but their adaptation to text remains challenging...

Precise genome editing process and its applications in plants driven by AI.

Genome editing technologies have emerged as the keystone of biotechnological research, enabling precise gene modification. The field has evolved rapid...