A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support

Introduction

Knowledge graphs and large language models (LLMs) serve as key tools for biomedical knowledge integration and reasoning, facilitating the structured organization of literature and the discovery of deep semantic relationships. However, existing methods still face challenges in knowledge mining and cross-document reasoning: knowledge graph construction is constrained ...

A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support

Introduction

Knowledge graphs and large language models (LLMs) serve as key tools for biomedical knowledge integration and reasoning, facilitating the structured organization of literature and the discovery of deep semantic relationships. However, existing methods still face challenges in knowledge mining and cross-document reasoning: knowledge graph construction is constrained ...

Using:

• vadr annotation (virus model must be selected in options)
• vardict variant caller
• coverage depth Provides summarizing files:
• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only

SINGLE-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT the mot ADAPTED VADR MODEL for annotation (see vadr parameters).

PAIRED-END workflow. Align reads on fasta reference/assembly using bwa mem, get a consensus, variants, mutation explanations.

IMPORTANT:

• For "bcftools call" consensus step, the --ploidy file is in "Données partagées" (Shared Data) and must be imported in your history to use the worflow by providing this file (tells bcftools to consider haploid variant calling).
• SELECT THE MOST ADAPTED VADR MODEL for annotation (see vadr parameters).

Using:

• vadr annotation (virus model must be selected in options)
• vardict variant caller
• coverage depth Provides summarizing files:
• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only

Using:

• vadr annotation (virus model must be selected in options)
• vardict variant caller
• coverage depth Provides summarizing files:
• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only (can be opened in Excel/LibreOffice)

Using:

• vadr annotation (virus model must be selected in options)
• vardict variant caller
• coverage depth Provides summarizing files:
• png image of variant calling with annotations and coverage depths
• tsv file with all information of significant variants only (can be opened in Excel/LibreOffice)

DeepAnnotation can be used to perform genomic selection (GS), which is a promising breeding strategy for agricultural breeding. DeepAnnotation predicts phenotypes from comprehensive multi-omics functional annotations with interpretable deep learning framework. The effectiveness of DeepAnnotation has been demonstrated in predicting three pork production traits (lean meat percentage at 100 kg [LMP], loin muscle depth at 100 kg [LMD], back fat thickness at 100 kg [BF]) on a population of 1940 Duroc ...

High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of AI brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, colour ...