Model: pavanmantha/bge-base-en-bioembed768
Author: pavanmantha
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:4247, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, en, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:BAAI/bge-base-en-v1.5, base_model:finetune:BAAI/bge-base-en-v1.5, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-base-en-v1.5
Card metadata: language: en
license: apache-2.0
library_name: sentence-transformers
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:4247, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
widget: source_sentence: The Opa1 protein localizes to the mitochondria.Opa1 is found normally in the mitochondrial intermembrane space.
sentences: Which is the cellular localization of the protein Opa1?, Which are the genes responsible for Dyskeratosis Congenita?, List blood marker for Non-Hodgkin lymphoma., source_sentence: CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities.We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs)Overall, it is demonstrated that the communication pathways could be multiple and intrinsically disposed, and the MC path generation approach provides an effective tool for the prediction of key residues that mediate the allosteric communication in an ensemble of pathways and functionally plausible residuesWe utilized a data set of 24 known allosteric sites from 23 monomer proteins to calculate the correlations between potential ligand-binding sites and corresponding orthosteric sites using a Gaussian network model (GNM)Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a proteinWe present a novel method, "MutInf", to identify statistically significant correlated motions from equilibrium molecular dynamics simulationsCorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities.Here, a Monte Carlo (MC) path generation approach is proposed and implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths.Here, a Monte Carlo (MC) path generation approach is proposed and implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths. Overall, it is demonstrated that the communication pathways could be multiple and intrinsically disposed, and the MC path generation approach provides an effective tool for the prediction of key residues that mediate the allosteric communication in an ensemble of pathways and functionally plausible residues We utilized a data set of 24 known allosteric sites from 23 monomer proteins to calculate the correlations between potential ligand-binding sites and corresponding orthosteric sites using a Gaussian network model (GNM)A Monte Carlo (MC) path generation approach is proposed and implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths. A novel method, "MutInf", to identify statistically significant correlated motions from equilibrium molecular dynamics simulations. CorrSite identifies potential alloster-binding sites based on motion correlation analyses between cavities. The Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a proteinComputational tools for predicting allosteric pathways in proteins include MCPath, MutInf, pySCA, CorrSite, and CARDS.
sentences: Computational tools for predicting allosteric pathways in proteins, What is PANTHER-PSEP?, What illness is transmitted by the Lone Star Tick, Amblyomma americanum?, source_sentence: Dopaminergic drugs should be given in patients with BMS. 
Catuama reduces the symptoms of BMS and may be a novel therapeutic strategy for the treatment of this disease.
Capsaicin, alpha-lipoic acid (ALA), and clonazepam were those that showed more reduction in symptoms of BMS.
Treatment with placebos produced a response that was 72% as large as the response to active drugs
sentences: What is the cyberknife used for?, Which compounds exist that are thyroid hormone analogs?, Which are the drugs utilized for the burning mouth syndrome?, source_sentence: Tinea is a superficial fungal infections of the skin.
sentences: Which molecule is targeted by a monoclonal antibody Mepolizumab?, What disease is tinea ?, Which algorithm is used for detection of long repeat expansions?, source_sentence: Basset is an open source package which applies CNNs to learn the functional activity of DNA sequences from genomics data. Basset was trained on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrated greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
sentences: Givosiran is used for treatment of which disease?, Describe the applicability of Basset in the context of deep learning, What is the causative agent of the "Panama disease" affecting bananas?
pipeline_tag: sentence-similarity
model-index: name: BGE base BioASQ Matryoshka
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 768
type: dim_768
metrics: type: cosine_accuracy@1
value: 0.8432203389830508
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9427966101694916
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.961864406779661
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9788135593220338
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8432203389830508
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3142655367231638
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19237288135593222
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.0978813559322034
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8432203389830508
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9427966101694916
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.961864406779661
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9788135593220338
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9167805960832026
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8963327280064567
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8971987609787653
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 512
type: dim_512
metrics: type: cosine_accuracy@1
value: 0.8538135593220338
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9427966101694916
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.961864406779661
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9745762711864406
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8538135593220338
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3142655367231638
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19237288135593222
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09745762711864407
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8538135593220338
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9427966101694916
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.961864406779661
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9745762711864406
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9198462326957965
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9016772598870054
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9026755533837086
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 256
type: dim_256
metrics: type: cosine_accuracy@1
value: 0.8453389830508474
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9385593220338984
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9555084745762712
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9745762711864406
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8453389830508474
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3128531073446327
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19110169491525425
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09745762711864407
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8453389830508474
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9385593220338984
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9555084745762712
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9745762711864406
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.914207272128957
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8944528517621736
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8952712251263324
name: Cosine Map@100
verified: no, task: type: information-retrieval
name: Information Retrieval
dataset: name: dim 128
type: dim_128
metrics: type: cosine_accuracy@1
value: 0.8220338983050848
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9279661016949152
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9449152542372882
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9703389830508474
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8220338983050848
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3093220338983051
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.18898305084745767
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09703389830508474
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8220338983050848
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9279661016949152
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9449152542372882
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9703389830508474
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.901534580728345
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8789800242130752
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8801051507894794
name: Cosine Map@100
verified: no
Likes: 1 | All-time downloads: 182
Created: 2024-08-08 09:09:00