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Bibliografia publikacji pracowników
Państwowej Szkoły Wyższej w Białej Podlaskiej

Baza tworzona przez Bibliotekę Akademii Bialskiej im. Jana Pawła II.


Zapytanie: METASTASIS
Liczba odnalezionych rekordów: 2



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Nr opisu: cek^sTomas^u^t^qVanecek T^w^x0000036941^zVanecek Tomas^aPaszkiewicz
Autorzy: , , , Justyna Mariusz Piotr Oryginalny artykuł naukowy publikacja bezkosztowa 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 46 CC-BY PaszkiewiczSacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network, Justyna Mariusz Piotr Oryginalny artykuł naukowy publikacja bezkosztowa 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM 009929.000 Q 003 Vol. 46 CC-BY PaszkiewiczSacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network.
Tytuł równoległy: SacharczukReligaoriginal-articleF000.00996100009996.2001467-3037003Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in PrecisionCurrent Issues in Molecular Biology20241467-30452023/2024https://doi.org/10.3390/cimb46080490Paszkiewicz, JustynaaiFINAL_PUBLISHEDThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.cancerclinical testcopy number variantgenomic markersmetastasisneural network : Justyna : Mariusz : Piotr : Oryginalny artykuł naukowy : publikacja bezkosztowa : 99929970.0000070.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR70.000PUNKTACJA UWM : 009929.000 : Q : 003 : Vol. 46 : CC-BY
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Słowa kluczowe ang.: TACJA UWM^a009996.200^b009929.000^c009999.000^d009929.000202420242024Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves00000481150000000208AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1467-3045/46/8/490100^a1467-3037^bQ^e1467-3045^iX^jXY^kQ004712^a003^b003^c2024-10-02, 10:59^d2024-11-22, 14:12^e3021029180^f3019828827^aUsing Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision^aCurrent Issues in Molecular Biology^a2024^bVol. 46^cissue 8^dp. 8301--8319^a1467-3045^a2023/2024^ahttps://doi.org/10.3390/cimb46080490^aPaszkiewicz, Justyna^cy^aai^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aThe accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.^acancer^aclinical test^acopy number variant^agenomic markers^ametastasis^aneural network
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Strony: RadziszewskiFraczekWolińskaPaszkiewiczReligaSacharczukF014101.00993999009994.1001422-0067003The Journey of Cancer Cells to the BrainInternational Journal of Molecular Sciences20231422-00672022/202310.3390/ijms24043854Paszkiewicz, JustynabraineFINAL_PUBLISHEDCancer metastases into the brain constitute one of the most severe, but not uncommon, manifestations of cancer progression. Several factors control how cancer cells interact with the brain to establish metastasis. These factors include mediators of signaling pathways participating in migration, infiltration of the blood-brain barrier, interaction with host cells (e.g., neurons, astrocytes), and the immune system. Development of novel therapies offers a glimpse of hope for increasing the diminutive life expectancy currently forecasted for patients suffering from brain metastasis. However, applying these treatment strategies has not been sufficiently effective. Therefore, there is a need for a better , Jakub, Karolina, Renata, Justyna, Piotr, Mariusz, utrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy, 998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM, 009859.000, Q, 003, Challenges and Opportunities, Vol. 24, CC-BY, , , , 017, , , 009999.000, 2023-03-15, 10:29, issue 4, y, AT_PUBLICATION, , , , WNZS0101, , , 009859.000202320232023Journey of Cancer Cells to the Brain Challenges and Opportunities00000450840000000466AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/4/3854100, 2024-06-25, 14:23, article number 3854
ISBN: Radziszewski
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Język publikacji: arczuk Mariusz^bMariusz^c^d^e^f^g^h^i ^m_^n_^oSacharczuk Mariusz^pSacharczuk Mariusz^rSacharczuk^sMariusz^u^t^qSacharczuk M^w^x0000026532^zSacharczuk MariuszŁazarczyk Marzena Mickael Michel Edward Skiba Dominik Kurzejamska Ewa Ławiński Michał Horbańczuk Jarosław Olav^aF01^butrzymanie i rozwój potencjału badawczego - art. 365 pkt 2 ustawy^a4101.00^11ACZartykuł w czasopiśmie zagranicznym4.900IF^a993999^b998599140.0000140.000PUNKTACJA KBNPUNKTACJA MINISTERSTWALISTA FILADELFIJSKAIMPACT FACTOR140.000PUNKTACJA UWM^a009994.100^b009859.000^c009999.000^d009859.000202320232023Journey of Cancer Cells to the Brain Challenges and Opportunities00000450840000000466AOartykuł oryginalny naukowyPUBLIKACJAPEŁNA PUBLIKACJAAAartykuł w czasopiśmie z IF (wykaz MNiSW)AFILIACJA PODANAENGhttps://www.mdpi.com/1422-0067/24/4/3854100^a1422-0067^bQ^iX^jXY^kQ009159^a003^b003^c2023-03-15, 10:29^d2024-06-25, 14:23^e3127899210^f3024798816^aThe Journey of Cancer Cells to the Brain^bChallenges and Opportunities^aInternational Journal of Molecular Sciences^a2023^bVol. 24^cissue 4^darticle number 3854^a1422-0067^a2022/2023^a10.3390/ijms24043854^aPaszkiewicz, Justyna^cy^abraine^aFINAL_PUBLISHED^bCC-BY^cAT_PUBLICATION^eOPEN_JOURNAL^aCancer metastases into the brain constitute one of the most severe, but not uncommon, manifestations of cancer progression. Several factors control how cancer cells interact with the brain to establish metastasis. These factors include mediators of signaling pathways participating in migration, infiltration of the blood-brain barrier, interaction with host cells (e.g., neurons, astrocytes), and the immune system. Development of novel therapies offers a glimpse of hope for increasing the diminutive life expectancy currently forecasted for patients suffering from brain metastasis. However, applying these treatment strategies has not been sufficiently effective. Therefore, there is a need for a better understanding of the metastasis process to uncover novel therapeutic targets. In this review, we follow the journey of various cancer cells from their primary location through the diverse processes that they undergo to colonize the brain. These processes include EMT, intravasation, extravasation, and infiltration of the blood-brain barrier, ending up with colonization and angiogenesis. In each phase, we focus on the pathways engaging molecules that potentially could be drug target candidates.^acancer^aimmune cells^ametastasis
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