1. P856 : a single-cell functional precision medicine landscape of multiple myelomaKlara Kropivšek, Paul Kachel, Sandra Goetze, Rebekka Wegmann, Yannik Severin, Benjamin D. Hale, Yasmin Festl, Julien Mena, Audrey Van Drogen, Nadja Dietliker, 2022, objavljeni povzetek znanstvenega prispevka na konferenci Opis: Multiple myeloma (MM) is a cancer of plasma cells, defined by complex genetics and extensive intra- and inter-patient heterogeneity. Despite improved patient survival driven by a plethora of treatment options, the disease remains incurable.
Molecularly-guided precision medicine to individualize treatment strategies in MM has had limited success, in part due to the genetic and molecular complexity of the disease. Functional precision medicine, a complementary approach in which patient treatment is guided by the ex vivo drug response of patient cells, has not yet been evaluated for MM systematically.
Ključne besede: mutliple myeloma, hematology, precision medicine, microscopy, deep learning, phenotyping, oncology, proteotype
Objavljeno v RUNG: 11.11.2024; Ogledov: 228; Prenosov: 2
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2. A roadmap of therapeutic strategies for patients with multiple myelomaBerend Snijder, Klara Kropivšek, 2023, drugi znanstveni članki Opis: Multiple myeloma is a rare and incurable cancer of plasma cells. To characterize this cancer, we developed an ex vivo drug screening method that combines imaging, deep learning and multiomics and applied it in an observational trial, uncovering new potential therapeutic strategies and underlying disease mechanisms.
Ključne besede: multiple myeloma, multiomics, deep learning, imaging, ex vivo drug screening
Objavljeno v RUNG: 11.11.2024; Ogledov: 234; Prenosov: 3
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3. Deep-learning-based cosmic-ray mass reconstruction using the water-Cherenkov and scintillation detectors of AugerPrimeNiklas Langner, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, objavljeni znanstveni prispevek na konferenci Opis: At the highest energies, cosmic rays can be detected only indirectly by the extensive air showers they create upon interaction with the Earth’s atmosphere. While high-statistics measurements of the energy and arrival directions of cosmic rays can be performed with large surface detector arrays like the Pierre Auger Observatory, the determination of the cosmic-ray mass on an event-by-event basis is challenging. Meaningful physical observables in this regard include the depth of maximum of air-shower profiles, which is related to the mean free path of the cosmic ray in the atmosphere and the shower development, as well as the number of muons that rises with the
number of nucleons in a cosmic-ray particle.
In this contribution, we present an approach to determine both of these observables from combined measurements of water-Cherenkov detectors and scintillation detectors, which are part of the AugerPrime upgrade of the Observatory. To characterize the time-dependent signals of the two detectors both separately as well as in correlation to each other, we apply deep learning techniques. Transformer networks employing the attention mechanism are especially well-suited for this task. We present the utilized network concepts and apply them to simulations to determine the precision of the event-by-event mass reconstruction that can be achieved by the combined measurements of the depth of shower maximum and the number of muons.
Ključne besede: Pierre Auger Observatory, ultra-high energy cosmic rays, muons, extensive air showers, surface detectors, AugerPrime, deep learning techiniques
Objavljeno v RUNG: 23.01.2024; Ogledov: 1297; Prenosov: 9
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4. Mass composition of Telescope Array's surface detectors events using deep learningI. Kharuk, Jon Paul Lundquist, 2022, objavljeni znanstveni prispevek na konferenci Opis: We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events.
Ključne besede: Telescope Array, indirect detection, ground array, surface detection, ultra-high energy, cosmic rays, composition, deep learning, machine learning, neural networks
Objavljeno v RUNG: 29.09.2023; Ogledov: 1748; Prenosov: 5
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5. Affinity maturation of antibody fragments : a review encompassing the development from random approaches to computational rational optimizationJiaqi Liu, Guangbo Kang, Jiewen Wang, Haibin Yuan, Yili Wu, Shuxian Meng, Ping Wang, Miao Zhang, Yuli Wang, Yuanhang Feng, He Huang, Ario De Marco, 2023, pregledni znanstveni članek Ključne besede: deep learning, protein modeling, random mutagenesis, rational mutagenesis
Objavljeno v RUNG: 17.07.2023; Ogledov: 1867; Prenosov: 8
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6. Localisation and classification of gamma ray sources using neural networksChris van den Oetelaar, Saptashwa Bhattacharyya, Boris Panes, Sascha Caron, Gabrijela Zaharijas, Roberto Ruiz de Austri, Guõlaugur Jóhannesson, 2021, objavljeni znanstveni prispevek na konferenci Opis: With limited statistics and spatial resolution of current detectors, accurately localising and separating gamma-ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the gamma-ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalised to other wavelengths. For the point source detection task, we use popular deep neural network structure U-NET, together with image segmentation, for precise localisation of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog, in addition to the background interstellar emission. Finally, we have created a more
complex but robust training data generation exploiting full detector potential, increasing spatial resolution at the highest energies.
Ključne besede: gamma-rays, deep learning, computer vision
Objavljeno v RUNG: 01.10.2021; Ogledov: 2600; Prenosov: 43
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7. Air-Shower Reconstruction at the Pierre Auger Observatory based on Deep LearningJonas Glombitza, Andrej Filipčič, Gašper Kukec Mezek, Samo Stanič, Marta Trini, Serguei Vorobiov, Lili Yang, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2019, objavljeni znanstveni prispevek na konferenci Ključne besede: Pierre Auger Observatory, extensive air showers, event reconstruction, deep learning
Objavljeno v RUNG: 16.06.2020; Ogledov: 3388; Prenosov: 83
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8. Exploring deep learning as an event classification method for the Cherenkov Telescope ArrayD. Nieto, Christopher Eckner, Gašper Kukec Mezek, Samo Stanič, Serguei Vorobiov, Lili Yang, Gabrijela Zaharijas, Danilo Zavrtanik, Marko Zavrtanik, 2017, objavljeni znanstveni prispevek na konferenci Ključne besede: CTA, event classification, deep learning
Objavljeno v RUNG: 16.02.2018; Ogledov: 4221; Prenosov: 148
Celotno besedilo (313,07 KB) |
