LEADER 03729nam 22005655 450 001 9910447250603321 005 20251113184408.0 010 $a3-030-64511-8 024 7 $a10.1007/978-3-030-64511-3 035 $a(CKB)4100000011643594 035 $a(DE-He213)978-3-030-64511-3 035 $a(MiAaPQ)EBC6421588 035 $a(PPN)252514505 035 $a(EXLCZ)994100000011643594 100 $a20201202d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical and Computational Oncology $eSecond International Symposium, ISMCO 2020, San Diego, CA, USA, October 8?10, 2020, Proceedings /$fedited by George Bebis, Max Alekseyev, Heyrim Cho, Jana Gevertz, Maria Rodriguez Martinez 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXII, 119 p. 34 illus., 25 illus. in color.) 225 1 $aLecture Notes in Bioinformatics,$x2366-6331 ;$v12508 311 08$a3-030-64510-X 327 $aInvited -- Plasticity in cancer cell populations: biology, mathematics and philosophy of cancer -- Statistical and Machine Learning Methods for Cancer Research -- CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer -- Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer -- Discriminative Localized Sparse Representations for Breast Cancer Screening -- Activation vs. Organization: Prognostic Implications of T and B cell Features of the PDAC Microenvironment -- On the use of neural networks with censored time-to-event data -- Mathematical Modeling for Cancer Research -- tugHall: a tool to reproduce Darwinian evolution of cancer cells for simulation-based personalized medicine -- General Cancer Computational Biology -- The potential of single cell RNA-sequencing data for the prediction of gastric cancer serum biomarkers -- Poster -- Theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant Detection -- Detecting subclones from spatially resolved RNA-seq data -- Novel driver synonymous mutations in the coding regions of GCB lymphoma patients improve the transcription levels of BCL2. 330 $aThis book constitutes the refereed proceedings of the Second International Symposium on Mathematical and Computational Oncology, ISMCO 2020, which was supposed to be held in San Diego, CA, USA, in October 2020, but was instead held virtually due to the COVID-19 pandemic. The 6 full papers and 4 short papers presented together with 1 invited talk were carefully reviewed and selected from 28 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; general cancer computational biology; and posters. 410 0$aLecture Notes in Bioinformatics,$x2366-6331 ;$v12508 606 $aComputer vision 606 $aArtificial intelligence 606 $aBioinformatics 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aComputational and Systems Biology 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aBioinformatics. 615 14$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aComputational and Systems Biology. 676 $a006.3 702 $aBebis$b George 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910447250603321 996 $aMathematical and computational oncology$92169006 997 $aUNINA