Review





Similar Products

ncbi reference sequence nm 014314 3  (Sino Biological)
94
Sino Biological ncbi reference sequence nm 014314 3
Ncbi Reference Sequence Nm 014314 3, supplied by Sino Biological, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/med_rxiv__64898__2026__03__02__26347088-226-5-12?v=Sino+Biological
Average 94 stars, based on 1 article reviews
ncbi reference sequence nm 014314 3 - by Bioz Stars, 2026-08
94/100 stars
  Buy from Supplier
human reference protein sequences  (Biotechnology Information)
86
Biotechnology Information human reference protein sequences
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Human Reference Protein Sequences, supplied by Biotechnology Information, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pmc12412403-112-12-20?v=Biotechnology+Information
Average 86 stars, based on 1 article reviews
human reference protein sequences - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier
primers designed against human telomere sequence or a single copy reference gene  (ScienCell)
90
ScienCell primers designed against human telomere sequence or a single copy reference gene
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Primers Designed Against Human Telomere Sequence Or A Single Copy Reference Gene, supplied by ScienCell, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pm40628762-105-8-22?v=ScienCell
Average 90 stars, based on 1 article reviews
primers designed against human telomere sequence or a single copy reference gene - by Bioz Stars, 2026-08
90/100 stars
  Buy from Supplier
ncbi reference sequence nm 020476  (OriGene)
90
OriGene ncbi reference sequence nm 020476
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Ncbi Reference Sequence Nm 020476, supplied by OriGene, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pmc12269838-394-12-5?v=OriGene
Average 90 stars, based on 1 article reviews
ncbi reference sequence nm 020476 - by Bioz Stars, 2026-08
90/100 stars
  Buy from Supplier
cellranger human reference sequence v2020 a  (10X Genomics)
86
10X Genomics cellranger human reference sequence v2020 a
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Cellranger Human Reference Sequence V2020 A, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pm40562033-351-22-27?v=10X+Genomics
Average 86 stars, based on 1 article reviews
cellranger human reference sequence v2020 a - by Bioz Stars, 2026-08
86/100 stars
  Buy from Supplier
human whole transcriptome v2.0 probe reference sequences  (BioSpyder Technologies)
90
BioSpyder Technologies human whole transcriptome v2.0 probe reference sequences
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Human Whole Transcriptome V2.0 Probe Reference Sequences, supplied by BioSpyder Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pmc12209759-106-7-14?v=BioSpyder+Technologies
Average 90 stars, based on 1 article reviews
human whole transcriptome v2.0 probe reference sequences - by Bioz Stars, 2026-08
90/100 stars
  Buy from Supplier
gapdh reference gene sequence  (OriGene)
93
OriGene gapdh reference gene sequence
Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, <t>human/virus</t> sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent <t>protein</t> biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein <t>sequences,</t> with outputs ranked to return the top five highest-confidence matches.
Gapdh Reference Gene Sequence, supplied by OriGene, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/human+reference+sequences/pm40933471-98-1-8?v=OriGene
Average 93 stars, based on 1 article reviews
gapdh reference gene sequence - by Bioz Stars, 2026-08
93/100 stars
  Buy from Supplier

Image Search Results


Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

Journal: Biosafety and Health

Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

doi: 10.1016/j.bsheal.2025.07.005

Figure Lengend Snippet: Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

Techniques: Virus, Sequencing

Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

Journal: Biosafety and Health

Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

doi: 10.1016/j.bsheal.2025.07.005

Figure Lengend Snippet: Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

Techniques: Generated, Standard Deviation