Medical Genetics
1개 벤치마크 · 논문 7편 · 이 태스크의 논문 보기 →
Benchmarks
BIG-bench
Most implemented
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Papers
GP-GPT: Large Language Model for Gene-Phenotype Mapping
Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits and heterogeneity of multi-sources genomi…
Information RetrievalLanguage ModelingLanguage ModellingLarge Language Model+1BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text
Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameters, are computationally expensive to run,…
ArticlesLanguage ModelingLanguage ModellingMedical Genetics+4Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling
In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presen…
Medical GeneticsCorrespondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al
We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which secondary findings should be sought (ACMG S…
DiagnosticMedical GeneticsScaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotype…
Deep LearningEfficient Neural NetworkGenetic Risk PredictionMedical Genetics