Genetics

Attacks on multimodal models

Today, models capable of working with various modalities simultaneously in a chat format are gaining increasing popularity. Despite this, there is an issue of potential attacks on these models, especially considering that many of them include open-source components. It is important to study whether the vulnerabilities of these components are inherited and how dangerous this can be when using suc...

SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics

Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability...

Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics

Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly...

Building a Risk Scoring Model for ARDS in Lung Adenocarcinoma Patients Using Machine Learning Algorithms.

Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced ma...

[LORENZO'S OIL AND ADRENOLEUKODYSTROPHY EXAMINING AN ARTIFICIAL INTELLIGENCE TOOL INTENDED FOR CONDUCTING LITERATURE SEARCHES AND ANALYSES].

Adrenoleukodystrophy is a genetic metabolic disorder characterized by a heterogeneous phenotype. Its severe form, known as cerebral adrenoleukodystrop...

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without muta...

Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics...

A machine learning approach to predict HPV positivity of oropharyngeal squamous cell carcinoma.

HPV status is an important prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC), with HPV-positive tumors associated with better overall...

Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

OBJECTIVE: CYP2D6 plays a critical role in metabolizing tamoxifen into its active metabolite, endoxifen, which is crucial for its therapeutic effect i...

MAFcounter: An efficient tool for counting the occurrences of k-mers in MAF files

Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative g...

DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

MOTIVATION: RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted dr...

Tiberius: end-to-end deep learning with an HMM for gene prediction.

MOTIVATION: For more than 25 years, learning-based eukaryotic gene predictors were driven by hidden Markov models (HMMs), which were directly inputted...

How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent ...

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significanc...

Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages.

Invertible promoters (invertons) are crucial regulatory elements in bacteria, facilitating gene expression changes under stress. Despite their importa...

HiCat: A Semi-Supervised Approach for Cell Type Annotation

We introduce HiCat (Hybrid Cell Annotation using Transformative embeddings), a novel semi-supervised pipeline for annotating cell types from single-...

ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics

Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across wh...

Prob-cGAN: A Probabilistic Conditional Generative Adversarial Network for LSD1 Inhibitor Activity Prediction

The inhibition of Lysine-Specific Histone Demethylase 1 (LSD1) is a promising strategy for cancer treatment and targeting epigenetic mechanisms. Thi...

BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.

Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the...