LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 108

Search options

  1. Book ; Online: Chembl antiviral ranked by cosine similarity to SARS-CoV-2 in KG-COVID-19 knowledge graph

    Reese, Justin

    2020  

    Abstract: Chembl antiviral ranked by cosine similarity to SARS-CoV-2 in KG-COVID-19 knowledge ... ...

    Abstract Chembl antiviral ranked by cosine similarity to SARS-CoV-2 in KG-COVID-19 knowledge graph
    Keywords covid19
    Publishing date 2020-08-29
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Book ; Online: Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), 80/20 training/test split, produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20

    Reese, Justin

    2020  

    Abstract: KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: These embeddings and weights were produced from this notebook on or around Sep 8, 2020: https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph% ...

    Abstract KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: These embeddings and weights were produced from this notebook on or around Sep 8, 2020: https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph%20embedding%20using%20SkipGram%20homogeneous%20graph.ipynb SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy SkipGram_80_20_training_test_epoch_500_delta_0.0001_weights.h5 I'm also including two runs just before this, with different epoch number and delta values: SkipGram_80_20_training_test_embedding_sep_6_2020_epoch_200_delta_0.001.npy SkipGram_80_20_training_test_weights_sep_6_2020_epoch_200_delta_0.001.h5 SkipGram_80_20_training_test_embedding_sep_7_2020_epoch_200_delta_0.0001.npy SkipGram_80_20_training_test_weights_sep_7_2020_epoch_200_delta_0.0001.h5
    Keywords covid19
    Publishing date 2020-09-08
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Book ; Online: Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20

    Reese, Justin

    2020  

    Abstract: Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = ... ...

    Abstract Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
    Keywords covid19
    Publishing date 2020-09-01
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: kg-covid-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data

    Reese, Justin

    2020  

    Abstract: KG-COVID-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 ... ...

    Abstract KG-COVID-19 graph in KGX TSV format, built on Sep 1, 2020, with no CORD-19 data
    Keywords covid19
    Publishing date 2020-09-02
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: MLP link prediction models in H5 format

    Reese, Justin

    2020  

    Abstract: The KG-COVID-19 graph from this Zenodo URL was used: https://zenodo.org/record/4011267/files/kg-covid-19-skipgram-aug-2020.tar.gz To produce these embeddings (Skipgram, 80/20 training/test split, seed=42, 500 epochs max, delta 0.0001) https://zenodo.org/ ... ...

    Abstract The KG-COVID-19 graph from this Zenodo URL was used: https://zenodo.org/record/4011267/files/kg-covid-19-skipgram-aug-2020.tar.gz To produce these embeddings (Skipgram, 80/20 training/test split, seed=42, 500 epochs max, delta 0.0001) https://zenodo.org/record/4019808/files/SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy These embeddings were used to train link prediction classifiers using this Jupyter notebook: https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Link%20prediction.ipynb SkipGram_weightedL2_finalized_model.h5 SkipGram_weightedL2.csv SkipGram_weightedL1_finalized_model.h5 SkipGram_weightedL1.csv SkipGram_hadamard_finalized_model.h5 SkipGram_hadamard.csv SkipGram_average_finalized_model.h5 SkipGram_average.csv all_reports.csv
    Keywords covid19
    Publishing date 2020-09-15
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Book ; Online: KG-COVID-19 graph containing all current sources EXCEPT ChEMBL antivirals

    Reese, Justin

    2020  

    Abstract: KG-COVID-19 graph containing all sources EXCEPT ChEMBL antivirals The follow commit was used to produce this KG on Sep 18, 2020 commit 9fd270e1b141487ee422d138cd74add87118f669 (HEAD -> no_chembl_merge, origin/master, origin/HEAD, ... ...

    Abstract KG-COVID-19 graph containing all sources EXCEPT ChEMBL antivirals The follow commit was used to produce this KG on Sep 18, 2020 commit 9fd270e1b141487ee422d138cd74add87118f669 (HEAD -> no_chembl_merge, origin/master, origin/HEAD, fix_internal_tabs_scibite_data) Merge: bf8a47a b545edc Author: Justin Reese <justaddcoffee+github@gmail.com> Date: Thu Sep 17 12:29:17 2020 -0700
    Keywords covid19
    Publishing date 2020-09-23
    Publishing country eu
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article ; Online: An evaluation of GPT models for phenotype concept recognition.

    Groza, Tudor / Caufield, Harry / Gration, Dylan / Baynam, Gareth / Haendel, Melissa A / Robinson, Peter N / Mungall, Christopher J / Reese, Justin T

    BMC medical informatics and decision making

    2024  Volume 24, Issue 1, Page(s) 30

    Abstract: Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on ... ...

    Abstract Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning methods) to curate patient profiles or existing scientific literature. With the significant shift in the use of large language models (LLMs) for most NLP tasks, we examine the performance of the latest Generative Pre-trained Transformer (GPT) models underpinning ChatGPT as a foundation for the tasks of clinical phenotyping and phenotype annotation.
    Materials and methods: The experimental setup of the study included seven prompts of various levels of specificity, two GPT models (gpt-3.5-turbo and gpt-4.0) and two established gold standard corpora for phenotype recognition, one consisting of publication abstracts and the other clinical observations.
    Results: The best run, using in-context learning, achieved 0.58 document-level F1 score on publication abstracts and 0.75 document-level F1 score on clinical observations, as well as a mention-level F1 score of 0.7, which surpasses the current best in class tool. Without in-context learning, however, performance is significantly below the existing approaches.
    Conclusion: Our experiments show that gpt-4.0 surpasses the state of the art performance if the task is constrained to a subset of the target ontology where there is prior knowledge of the terms that are expected to be matched. While the results are promising, the non-deterministic nature of the outcomes, the high cost and the lack of concordance between different runs using the same prompt and input make the use of these LLMs challenging for this particular task.
    MeSH term(s) Humans ; Knowledge ; Language ; Machine Learning ; Phenotype ; Rare Diseases
    Language English
    Publishing date 2024-01-31
    Publishing country England
    Document type Journal Article
    ZDB-ID 2046490-3
    ISSN 1472-6947 ; 1472-6947
    ISSN (online) 1472-6947
    ISSN 1472-6947
    DOI 10.1186/s12911-024-02439-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article: On the limitations of large language models in clinical diagnosis.

    Reese, Justin T / Danis, Daniel / Caufield, J Harry / Groza, Tudor / Casiraghi, Elena / Valentini, Giorgio / Mungall, Christopher J / Robinson, Peter N

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Objective: Large Language Models such as GPT-4 previously have been applied to differential diagnostic challenges based on published case reports. Published case reports have a sophisticated narrative style that is not readily available from typical ... ...

    Abstract Objective: Large Language Models such as GPT-4 previously have been applied to differential diagnostic challenges based on published case reports. Published case reports have a sophisticated narrative style that is not readily available from typical electronic health records (EHR). Furthermore, even if such a narrative were available in EHRs, privacy requirements would preclude sending it outside the hospital firewall. We therefore tested a method for parsing clinical texts to extract ontology terms and programmatically generating prompts that by design are free of protected health information.
    Materials and methods: We investigated different methods to prepare prompts from 75 recently published case reports. We transformed the original narratives by extracting structured terms representing phenotypic abnormalities, comorbidities, treatments, and laboratory tests and creating prompts programmatically.
    Results: Performance of all of these approaches was modest, with the correct diagnosis ranked first in only 5.3-17.6% of cases. The performance of the prompts created from structured data was substantially worse than that of the original narrative texts, even if additional information was added following manual review of term extraction. Moreover, different versions of GPT-4 demonstrated substantially different performance on this task.
    Discussion: The sensitivity of the performance to the form of the prompt and the instability of results over two GPT-4 versions represent important current limitations to the use of GPT-4 to support diagnosis in real-life clinical settings.
    Conclusion: Research is needed to identify the best methods for creating prompts from typically available clinical data to support differential diagnostics.
    Language English
    Publishing date 2024-02-26
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.07.13.23292613
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Article ; Online: The promises of large language models for protein design and modeling.

    Valentini, Giorgio / Malchiodi, Dario / Gliozzo, Jessica / Mesiti, Marco / Soto-Gomez, Mauricio / Cabri, Alberto / Reese, Justin / Casiraghi, Elena / Robinson, Peter N

    Frontiers in bioinformatics

    2023  Volume 3, Page(s) 1304099

    Abstract: The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the "language of proteins" ...

    Abstract The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the "language of proteins" invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.
    Language English
    Publishing date 2023-11-23
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2023.1304099
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Article ; Online: Using knowledge graphs to infer gene expression in plants.

    Thessen, Anne E / Cooper, Laurel / Swetnam, Tyson L / Hegde, Harshad / Reese, Justin / Elser, Justin / Jaiswal, Pankaj

    Frontiers in artificial intelligence

    2023  Volume 6, Page(s) 1201002

    Abstract: Introduction: Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood ... ...

    Abstract Introduction: Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through
    Methods: We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in
    Results: A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.
    Discussion: This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.
    Language English
    Publishing date 2023-06-13
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2023.1201002
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top