sentence-transformers How to use johnnySoftData/distilroberta-ai-bio-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("johnnySoftData/distilroberta-ai-bio-embeddings")
sentences = [
"The proposed HBI framework improves upon previous statistical methods by reducing parameter errors, resisting outliers, and avoiding biases toward overly simplistic models.",
"Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.",
"Long interspersed element type 1 (LINE-1, also L1 for short) is the only autonomously transposable element in the human genome. Its insertion into a new genomic site may disrupt the function of genes, potentially causing genetic diseases. Cells have thus evolved a battery of mechanisms to tightly control LINE-1 activity. Here, we report that a cellular antiviral protein, myxovirus resistance protein B (MxB), restricts the mobilization of LINE-1. This function of MxB requires the nuclear localization signal located at its N-terminus, its GTPase activity and its ability to form oligomers. We further found that MxB associates with LINE-1 protein ORF1p and promotes sequestration of ORF1p to G3BP1-containing cytoplasmic granules. Since knockdown of stress granule marker proteins G3BP1 or TIA1 abolishes MxB inhibition of LINE-1, we conclude that MxB engages stress granule components to effectively sequester LINE-1 proteins within the cytoplasmic granules, thus hindering LINE-1 from accessing the nucleus to complete retrotransposition. Thus, MxB protein provides one mechanism for cells to control the mobility of retroelements.",
"During meiosis, homologous chromosomes recognize each other, align, and exchange genetic information. This process requires the action of RecA-related proteins Rad51 and Dmc1 to catalyze DNA strand exchanges. The Mnd1–Hop2 complex has been shown to assist in Dmc1-dependent processes. Furthermore, higher eukaryotes possess additional RecA-related proteins, like XRCC3, which are involved in meiotic recombination. However, little is known about the functional interplay between these proteins during meiosis. We investigated the functional relationship between AtMND1, AtDMC1, AtRAD51, and AtXRCC3 during meiosis in Arabidopsis thaliana. We demonstrate the localization of AtMND1 to meiotic chromosomes, even in the absence of recombination, and show that AtMND1 loading depends exclusively on AHP2, the Arabidopsis Hop2 homolog. We provide evidence of genetic interaction between AtMND1, AtDMC1, AtRAD51, and AtXRCC3. In vitro assays suggest that this functional link is due to direct interaction of the AtMND1–AHP2 complex with AtRAD51 and AtDMC1. We show that AtDMC1 foci accumulate in the Atmnd1 mutant, but are reduced in number in Atrad51 and Atxrcc3 mutants. This study provides the first insights into the functional differences of AtRAD51 and AtXRCC3 during meiosis, demonstrating that AtXRCC3 is dispensable for AtDMC1 focus formation in an Atmnd1 mutant background, whereas AtRAD51 is not. These results clarify the functional interactions between key players in the strand exchange processes during meiotic recombination. Furthermore, they highlight a direct interaction between MND1 and RAD51 and show a functional divergence between RAD51 and XRCC3."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
