Genetics

An Evolutional Neural Network Framework for Classification of Microarray Data

DNA microarray gene-expression data has been widely used to identify cancerous gene signatures. Microarray can increase the accuracy of cancer diagnosis and prognosis. However, analyzing the large amount of gene expression data from microarray chips pose a challenge for current machine learning researches. One of the challenges lie within classification of healthy and cancerous tissues is high d...

RatGene: Gene deletion-addition algorithms using growth to production ratio for growth-coupled production in constraint-based metabolic networks

In computational metabolic design, it is often necessary to modify the original constraint-based metabolic networks to lead to growth-coupled production, where cell growth forces target metabolite production. However, in genome-scale models, finding strategies to simultaneously delete and add genes to induce growth-coupled production is challenging. This is particularly true when heavy computati...

Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space

The advancement of novel combinatorial CRISPR screening technologies enables the identification of synergistic gene combinations on a large scale. T...

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

The continually increasing volume of DNA sequence data has resulted in a growing demand for fast implementations of core algorithms. Computation of ...

A Modular Open Source Framework for Genomic Variant Calling

Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs)...

Leveraging genomic deep learning models for non-coding variant effect prediction

The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function r...

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective treatment for POAG therapy. Thus, identifying some ...

Comparative genomics with succinct colored de Bruijn graphs

DNA technologies have evolved significantly in the past years enabling the sequencing of a large number of genomes in a short time. Nevertheless, th...

DBgDel: Database-Enhanced Gene Deletion Framework for Growth-Coupled Production in Genome-Scale Metabolic Models

When simulating metabolite productions with genome-scale constraint-based metabolic models, gene deletion strategies are necessary to achieve growth...

Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows

Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the opti...

Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+

Genetic screens mediated via CRISPR-Cas9 combined with high-content readouts have emerged as powerful tools for biological discovery. However, compu...

MimIR: An Extensible and Type-Safe Intermediate Representation for the DSL Age

Traditional compilers, designed for optimizing low-level code, fall short when dealing with modern, computation-heavy applications like image proces...

White-Box Diffusion Transformer for single-cell RNA-seq generation

As a powerful tool for characterizing cellular subpopulations and cellular heterogeneity, single cell RNA sequencing (scRNA-seq) technology offers a...

m6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous ...

Integrating Large Language Models for Genetic Variant Classification

The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics an...

Perspective on recent developments and challenges in regulatory and systems genomics

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulato...

Safe Paths and Sequences for Scalable ILPs in RNA Transcript Assembly Problems

A common step at the core of many RNA transcript assembly tools is to find a set of weighted paths that best explain the weights of a DAG. While suc...

Accelerating DNA Read Mapping with Digital Processing-in-Memory

Genome analysis has revolutionized fields such as personalized medicine and forensics. Modern sequencing machines generate vast amounts of fragmente...

Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.

In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large b...

Exploring the Potentials and Challenges of Using Large Language Models for the Analysis of Transcriptional Regulation of Long Non-coding RNAs

Research on long non-coding RNAs (lncRNAs) has garnered significant attention due to their critical roles in gene regulation and disease mechanisms....