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Book ; Online: finnkuusisto/covid19_word_embedding

Finn Kuusisto

First release for publication.

2020

Abstract: This is the first release of code and data for submission to F1000 for Word Embedding Mining for SARS-CoV-2 and COVID-19 Drug Repurposing. ...

Abstract	This is the first release of code and data for submission to F1000 for Word Embedding Mining for SARS-CoV-2 and COVID-19 Drug Repurposing.
Keywords	Drug repurposing ; COVID-19 ; SARS-CoV-2 ; Word embedding ; Text Mining ; covid19
Publishing date	2020-05-27
Publishing country	eu
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biomedical Literature Mining for Repurposing Laboratory Tests.

Kuusisto, Finn / Kleiman, Ross / Weiss, Jeremy

Methods in molecular biology (Clifton, N.J.)

2022 Volume 2496, Page(s) 91–109

Abstract: Epidemiological studies identifying biological markers of disease state are valuable, but can be time-consuming, expensive, and require extensive intuition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggesting ... ...

Abstract	Epidemiological studies identifying biological markers of disease state are valuable, but can be time-consuming, expensive, and require extensive intuition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggesting that high-quality initial hypotheses are crucial. In this chapter, we describe a high-throughput pipeline to produce a ranked list of high-quality hypothesized biomarkers for diseases. We review an example use of this approach to generate a large number of candidate disease biomarker hypotheses derived from machine learning models, filter and rank them according to their potential novelty using text mining, and corroborate the most promising hypotheses with further statistical modeling. The example use of the pipeline uses a large electronic health record dataset and the PubMed corpus, to find several promising hypothesized laboratory tests with previously undocumented correlations to particular diseases.
MeSH term(s)	Data Mining ; Electronic Health Records ; Machine Learning ; Models, Statistical ; Publications
Language	English
Publishing date	2022-06-16
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-2305-3_5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues.

Kuusisto, Finn / Costa, Vitor Santos / Hou, Zhonggang / Thomson, James / Page, David / Stewart, Ron

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications

2020 Volume 2019, Page(s) 293–298

Abstract: There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such ... ...

Abstract	There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as
Language	English
Publishing date	2020-02-17
Publishing country	United States
Document type	Journal Article
DOI	10.1109/icmla.2019.00055
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models.

Millikin, Robert J / Raja, Kalpana / Steill, John / Lock, Cannon / Tu, Xuancheng / Ross, Ian / Tsoi, Lam C / Kuusisto, Finn / Ni, Zijian / Livny, Miron / Bockelman, Brian / Thomson, James / Stewart, Ron

bioRxiv : the preprint server for biology

2023

Abstract: Background: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable ... ...

Abstract	Background: The PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.
Language	English
Publishing date	2023-06-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.30.542911
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 412

Abstract: Background: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools ...

Abstract	Background: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. Conclusions: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.
MeSH term(s)	Humans ; Algorithms ; Neoplasms ; PubMed ; Knowledge ; Knowledge Discovery
Language	English
Publishing date	2023-11-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05539-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Problems Experienced by Health Care Professionals with Do not Attempt Resuscitation (DNAR) Orders - A Qualitative Study.

Kuusisto, Hanna / Keränen, Tapani / Saranto, Kaija

Studies in health technology and informatics

2023 Volume 309, Page(s) 233–237

Abstract: ... a catchment area of 900,000 Finns. The questionnaire covered issues on DNAR order making, its meaning and ...

Abstract	A 'Do Not Attempt Resuscitation' (DNAR) order is one of the most important yet difficult medical decisions. Despite the recent European guidelines, health care professionals (HCPs) in general perceive challenges in making a DNAR order. We aimed to evaluate the types of problems related to DNAR order making. A link to a web-based multiple-choice questionnaire including open-ended questions was sent by e-mail to all physicians and nurses working in the Tampere University Hospital special responsibility area covering a catchment area of 900,000 Finns. The questionnaire covered issues on DNAR order making, its meaning and documentation. Here we report the analysis of the open-ended questions, examined based on the Ottawa Decision Support Framework with expanded individual decisional needs categories. Qualitative data describing respondents' opinions (N=648) regarding problems related to DNAR order decision making were analysed using Atlas.ti 23.12 software. In total, 599 statements (phrases) dealing with inadequate advice, information, emotional support, and instrumental help were identified. Our results show that HCPs experience lack of support in DNAR decision making on multiple levels. Digital decision-making support integrated into electronic patient records (EPR) to assure timely and clearly visible DNAR orders could be beneficial.
MeSH term(s)	Humans ; Resuscitation Orders/psychology ; Physicians ; Surveys and Questionnaires ; Hospitals, University ; Qualitative Research
Language	English
Publishing date	2023-11-01
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI230785
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample.

Movaghar, Arezoo / Page, David / Scholze, Danielle / Hong, Jinkuk / DaWalt, Leann Smith / Kuusisto, Finn / Stewart, Ron / Brilliant, Murray / Mailick, Marsha

Genetics in medicine : official journal of the American College of Medical Genetics

2021 Volume 23, Issue 7, Page(s) 1273–1280

Abstract: Purpose: Fragile X syndrome (FXS), the most prevalent inherited cause of intellectual disability, remains underdiagnosed in the general population. Clinical studies have shown that individuals with FXS have a complex health profile leading to unique ... ...

Abstract	Purpose: Fragile X syndrome (FXS), the most prevalent inherited cause of intellectual disability, remains underdiagnosed in the general population. Clinical studies have shown that individuals with FXS have a complex health profile leading to unique clinical needs. However, the full impact of this X-linked disorder on the health of affected individuals is unclear and the prevalence of co-occurring conditions is unknown. Methods: We mined the longitudinal electronic health records from more than one million individuals to investigate the health characteristics of patients who have been clinically diagnosed with FXS. Additionally, using machine-learning approaches, we created predictive models to identify individuals with FXS in the general population. Results: Our discovery-oriented approach identified the associations of FXS with a wide range of medical conditions including circulatory, endocrine, digestive, and genitourinary, in addition to mental and neurological disorders. We successfully created predictive models to identify cases five years prior to clinical diagnosis of FXS without relying on any genetic or familial data. Conclusion: Although FXS is often thought of primarily as a neurological disorder, it is in fact a multisystem syndrome involving many co-occurring conditions, some primary and some secondary, and they are associated with a considerable burden on patients and their families.
MeSH term(s)	Artificial Intelligence ; Fragile X Syndrome/diagnosis ; Fragile X Syndrome/epidemiology ; Fragile X Syndrome/genetics ; Humans ; Intellectual Disability/diagnosis ; Intellectual Disability/epidemiology ; Intellectual Disability/genetics ; Machine Learning ; Phenotype
Language	English
Publishing date	2021-03-26
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1455352-1
ISSN	1530-0366 ; 1098-3600
ISSN (online)	1530-0366
ISSN	1098-3600
DOI	10.1038/s41436-021-01144-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models

Robert J. Millikin / Kalpana Raja / John Steill / Cannon Lock / Xuancheng Tu / Ian Ross / Lam C. Tsoi / Finn Kuusisto / Zijian Ni / Miron Livny / Brian Bockelman / James Thomson / Ron Stewart

BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

2023 Volume 14

Abstract: Abstract Background The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and ... ...

Abstract	Abstract Background The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A–B–C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. Results We demonstrate SKiM’s ability to discover useful A–B–C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and ...
Keywords	Literature-based discovery ; Knowledge graph ; Biomedical text mining ; Relation extraction ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Extraction and Visualization of Protein-Protein Interaction Networks and Beyond: A Text-Mining Protocol.

Raja, Kalpana / Natarajan, Jeyakumar / Kuusisto, Finn / Steill, John / Ross, Ian / Thomson, James / Stewart, Ron

Methods in molecular biology (Clifton, N.J.)

2019 Volume 2074, Page(s) 13–34

Abstract: Proteins perform their functions by interacting with other proteins. Protein-protein interaction (PPI) is critical for understanding the functions of individual proteins, the mechanisms of biological processes, and the disease mechanisms. High-throughput ...

Abstract	Proteins perform their functions by interacting with other proteins. Protein-protein interaction (PPI) is critical for understanding the functions of individual proteins, the mechanisms of biological processes, and the disease mechanisms. High-throughput experiments accumulated a huge number of PPIs in PubMed articles, and their extraction is possible only through automated approaches. The standard text-mining protocol includes four major tasks, namely, recognizing protein mentions, normalizing protein names and aliases to unique identifiers such as gene symbol, extracting PPIs, and visualizing the PPI network using Cytoscape or other visualization tools. Each task is challenging and has been revised over several years to improve the performance. We present a protocol based on our hybrid approaches and show the possibility of presenting each task as an independent web-based tool, NAGGNER for protein name recognition, ProNormz for protein name normalization, PPInterFinder for PPI extraction, and HPIminer for PPI network visualization. The protocol is specific to human but can be generalized to other organisms. We include KinderMiner, our most recent text-mining tool that predicts PPIs by retrieving significant co-occurring protein pairs. The algorithm is simple, easy to implement, and generalizable to other biological challenges.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Data Mining ; Databases, Protein ; Protein Interaction Mapping ; Protein Interaction Maps ; Software
Language	English
Publishing date	2019-10-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-9873-9_2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Healthcare resource use of patients with transthyretin amyloid cardiomyopathy.

Lauppe, Rosa / Liseth Hansen, Johan / Fornwall, Anna / Johansson, Katarina / Rozenbaum, Mark H / Strand, Anne Mette / Vakevainen, Merja / Kuusisto, Johanna / Gude, Einar / Smith, J Gustav / Gustafsson, Finn

ESC heart failure

2022 Volume 9, Issue 3, Page(s) 1636–1642

Abstract: Aims: Transthyretin amyloid cardiomyopathy (ATTR-CM) is the cardiac manifestation of transthyretin amyloidosis (ATTR). The aim of this study was to estimate healthcare resource use for ATTR-CM patients compared with heart failure (HF) patients, in ... ...

Abstract	Aims: Transthyretin amyloid cardiomyopathy (ATTR-CM) is the cardiac manifestation of transthyretin amyloidosis (ATTR). The aim of this study was to estimate healthcare resource use for ATTR-CM patients compared with heart failure (HF) patients, in Denmark, Finland, Norway, and Sweden. Methods and results: Data from nationwide healthcare registers in the four countries were used. ATTR-CM patients were defined as individuals diagnosed with amyloidosis and cardiomyopathy or HF between 2008 and 2018. Patients in the ATTR-CM cohort were matched to patients with HF but without ATTR-CM diagnosis. Resource use included number of visits to specialty outpatient and inpatient hospital care. A total of 1831 ATTR-CM and 1831 HF patients were included in the analysis. The mean number of hospital-based healthcare contacts increased in both the ATTR-CM and HF cohort during 3 years pre-diagnosis and was consistently higher for the ATTR-CM cohort compared with the HF cohort, with 6.1 [CI: 5.9-6.3] vs. 3.2 [CI: 3.1-3.3] outpatient visits and 1.03 [CI: 0.96-1.1] vs. 0.7 [CI: 0.7-0.8] hospitalizations. In the first year following diagnosis, patients with ATTR-CM continued to visit outpatient care (10.2 [CI: 10.1, 10.4] vs. 5.7 [CI: 5.6, 5.9]) and were admitted to hospital more frequently (3.3 [CI: 3.2, 3.4] vs. 2.5 [CI: 2.5, 2.6]) than HF patients. Conclusions: Transthyretin amyloid cardiomyopathy imposes a high burden on healthcare systems with twice as many outpatient specialist visits and 50% more hospitalizations in the year after diagnosis compared with HF patients without ATTR-CM. Studies to investigate if earlier diagnosis and treatment of ATTR-CM may lower resource use are warranted.
MeSH term(s)	Amyloid Neuropathies, Familial/complications ; Amyloid Neuropathies, Familial/diagnosis ; Amyloid Neuropathies, Familial/epidemiology ; Cardiomyopathies/diagnosis ; Cardiomyopathies/epidemiology ; Cardiomyopathies/therapy ; Delivery of Health Care ; Heart Failure/diagnosis ; Heart Failure/epidemiology ; Heart Failure/therapy ; Humans ; Prealbumin
Chemical Substances	Prealbumin
Language	English
Publishing date	2022-04-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2814355-3
ISSN	2055-5822 ; 2055-5822
ISSN (online)	2055-5822
ISSN	2055-5822
DOI	10.1002/ehf2.13913
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