Genetic Variant

Genetic variants, variations in DNA sequences among individuals, are central to understanding disease susceptibility and phenotypic diversity. Current research focuses on developing robust methods for analyzing and interpreting these variants, leveraging machine learning techniques like graph neural networks and deep learning models to analyze large-scale genomic datasets and predict variant effects. This work is crucial for improving the accuracy of polygenic risk scores, facilitating personalized medicine, and advancing our understanding of complex diseases by integrating genomic data with other modalities like neuroimaging. The ultimate goal is to translate these advancements into improved diagnostics, prognostics, and treatment strategies.

Papers

November 7, 2024

Integrating Large Language Models for Genetic Variant Classification
Youssef Boulaimen, Gabriele Fossi, Leila Outemzabet, Nathalie Jeanray, Oleksandr Levenets, Stephane Gerart, Sebastien Vachenc, Salvatore Raieli, Joanna Giemza
Genetic Variant Depth Based Variant Search Algorithm

July 30, 2024

A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
Shivika Prasanna, Ajay Kumar, Deepthi Rao, Eduardo Simoes, Praveen Rao
Machine Learning Knowledge Graph Real Human Graph Machine Learning Genetic Variant Scalable Solution

July 24, 2024

GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning
Zehui Li, Vallijah Subasri, Guy-Bart Stan, Yiren Zhao, Bo Wang
Foundation Model Large Scale Dataset Voronoi Tessellation Genetic Variant Gene Representation

June 14, 2024

Challenges in explaining deep learning models for data with biological variation
Lenka Tětková, Erik Schou Dreier, Robin Malm, Lars Kai Hansen
Deep Learning Model Machine Learning Model Technical Challenge Raw Data Benchmark Dataset Explainability Method Post Hoc Explainability Genetic Variant

May 10, 2024

Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction
Aleix Lafita, Ferran Gonzalez, Mahmoud Hossam, Paul Smyth, Jacob Deasy, Ari Allyn-Feuer, Daniel Seaton, Stephen Young
Protein Language Model Genetic Variant Variant Effect Prediction

February 2, 2024

PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks
Jose L. Mellina Andreu, Luis Bernal, Antonio F. Skarmeta, Mina Ryten, Sara Álvarez, Alejandro Cisterna García, Juan A. Botía
Line by Line Explanation Heterogeneous Graph Neural Network Genetic Variant Genotype Phenotype Several Distinct Phenotype

January 31, 2024

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration
Kotaro Yoshida, Hiroki Naganuma
Camera Lens Calibration Performance Alpha Invariance Invariant Risk Minimization Model Invariance Genetic Variant

January 7, 2024

Dynamics-based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction
Majd Al Aawar, Srikar Mutnuri, Mansooreh Montazerin, Ajitesh Srivastava
Graph Neural Network Dynamic Graph Neural Network Epidemic Model Variant Pathogenicity Theoretical Peak Performance Analysis Genetic Variant Quantifying Emergence

December 21, 2023

Preparing to Integrate Generative Pretrained Transformer Series 4 models into Genetic Variant Assessment Workflows: Assessing Performance, Drift, and Nondeterminism Characteristics Relative to Classifying Functional Evidence in Literature
Samuel J. Aronson, Kalotina Machini, Jiyeon Shin, Pranav Sriraman, Sean Hamill, Emma R. Henricks, Charlotte Mailly, Angie J. Nottage, Sami S. Amr, Michael Oates, Matthew S. Lebo
Full Model Generative Pre Trained Transformer Predictive Performance Empirical Evidence Variant Pathogenicity Genetic Variant Depth Based Variant Search Algorithm

December 7, 2023

Scalable Knowledge Graph Construction and Inference on Human Genome Variants
Shivika Prasanna, Deepthi Rao, Eduardo Simoes, Praveen Rao
Knowledge Graph Scientific Inference Large Knowledge Graph Genetic Variant Genomic Data

August 8, 2023

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder
Nicha C. Dvornek, Catherine Sullivan, James S. Duncan, Abha R. Gupta
Human Prediction Multimodal Data Functional Magnetic Resonance Imaging Autism Spectrum Disorder Genetic Variant Neuroimaging Study

July 24, 2023

Deep neural network improves the estimation of polygenic risk scores for breast cancer
Adrien Badré, Li Zhang, Wellington Muchero, Justin C. Reynolds, Chongle Pan
Deep Neural Network Estimation Task Breast Cancer Genetic Variant Polygenic Risk

July 3, 2023

Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines
Ellery Smith, Rahel Paloots, Dimitris Giagkos, Michael Baudis, Kurt Stockinger
Information Extraction Molecular Data BioMedical Literature Evidence Based Tumor Cell Genetic Variant Content Enrichment Discourse Entity

March 8, 2023

Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model
Juan Guerrero Montero, Richard A. Blythe
Scientific Inference Beta Distribution Genetic Variant Fisher Weighted Fitness Approximation

January 27, 2023

Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling
Suha Tuna, Cagri Gulec, Emrah Yucesan, Ayse Cirakoglu, Yelda Tarkan Arguden
Global Evaluation High Dimensional Natural Language Oriented Variant Genetic Variant Cell Graph Human Team Gene Network

November 4, 2022

The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation
Barbara Plank
Global Evaluation Raw Data Ground Truth Genetic Variant Human Label Variation

October 23, 2022

Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders
Ömer Sümer, Fabio Hellmann, Alexander Hustinx, Tzung-Chien Hsieh, Elisabeth André, Peter Krawitz
Representation Learning Face Recognition Model Genetic Variant

October 21, 2022

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Hansheng Xue, Vaibhav Rajan, Yu Lin
Neural Network Microbial Genome Graph Coloring Genetic Variant

November 19, 2021

SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes
Yiming Li, Sanjiv J. Shah, Donna Arnett, Ryan Irvin, Yuan Luo
Human Prediction Genetic Variant Hypertension Subtypes

Genetic Variant

Papers

Integrating Large Language Models for Genetic Variant Classification

A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning

GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning

Challenges in explaining deep learning models for data with biological variation

Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction

PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks

Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration

Dynamics-based Feature Augmentation of Graph Neural Networks for Variant Emergence Prediction

Preparing to Integrate Generative Pretrained Transformer Series 4 models into Genetic Variant Assessment Workflows: Assessing Performance, Drift, and Nondeterminism Characteristics Relative to Classifying Functional Evidence in Literature

Scalable Knowledge Graph Construction and Inference on Human Genome Variants

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines

Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model

Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling

The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation

Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes