dataesr/scientific-paragraphs-categorization huggingface dataset:
At this stage, statements about the current performance of ML-based DRP methods overall are difficult to make. In the clinic, molecular patient data are already an established input for treatment decisions  [152] , and the potential of ML-based DRP models to predict tumoral responses to anticancer drugs has been established  [30, 44, 46, 58, 67, 71, 101]. However, ML-based DRP seems to be rarely used in clinical practice. One major reason for the currently limited utility of DRP in the clinic may be the difficulty of addressing random and systematic variations between datasets, which is an issue in many applications of ML. Another reason may be the lack of interpretability of many DRP models in the context of other rich, and often unstructured, relevant additional clinical information that is currently not used by DRP models.
The future of drug prioritization in the clinic will undoubtedly be influenced by ML  [153]. We believe that future improvements-especially, towards the use of DRP models in the clinicwill be catalyzed in a few distinct ways. Firstly, it can be expected that, as in other ML applications, larger systematic datasets will be able to drive performance increases, especially if new datasets are aimed at the more comprehensive characterization of the relevant biology. This is particularly true if one considers the current paucity of systematic data outside of the cell line context. Future datasets with the largest positive impact on DRP performance may therefore well come from advanced preclinical models. Although more, and more diverse, data can be thought to help in any ML approach, it is in particular deep learning, as a maximally flexible class of models, that is fundamentally capable of capturing the more subtle characteristics of larger datasets.
The increasing availability of computing power-and computing as a part of the biomedical research enterprise-will likely enable the continued application of new, and computationally ambitious, ML technology to DRP. A second, complementary, route towards higher performance of DRP models is the incorporation of prior knowledge. Until now, the incorporation of prior knowledge through biomolecular networks has led to only slight overall improvements at best  [44, 49, 60]. However, Oskooei et al. [84]  has found clear improvements for specific drugs, when additional information about drug targets was provided. Various missing aspects of drug responses in the clinic may be addressable by either large datasets, or the incorporation of knowledge (or a combination of both), as long as various current systematic limitations of data and models are addressed.
The state of the immune system in relevant tissue can be highly associated with chemotherapy resistance  [158] , and immunomodulators have, in the last decade, become major weapons themselves against cancer and other diseases  [159]. The dbGap database  [160]  covers immune responses to drugs with RECIST parameters, and thus provides future opportunities for the incorporation of the immune system into DRP. The microbiome is yet another complex part of human physiology known to regulate many aspects of human health, and, increasingly, it is also recognized as a mediator of drug effects  [161, 162]. Evolving insight into the microbiome and its role in drug action may stimulate the adoption of the microbiome as part of DRP models  [163] , or lead to models in which the microbiome can then either act as an enhancer, or even become itself an active part of therapy through the molecules it secretes  [164].
Related datasets, such as TCGA  [25]  microbiome data have, so far, not been included in DRP approaches. Another area of potential future significance for machine learning-based DRP is the prediction of unwanted side effects or adverse drug reactions. Although methods for the prediction of more general adverse patient events currently appear to focus on electronic health records  [165] , increasing insight into the molecular basis of adverse drug reactions  [166]  and associated data may also permit the future integration with principles used today for DRP. Eventually, DRP methods and their underlying principles may also be applicable to the study of environmental-or food-related molecules on human health. Perhaps spurred by the human genome as the exhaustive parts list of human biomolecules, environmental science has started to adopt comprehensive approaches to profile exogenous chemicals, and to study their impact on human health  [167].
Box I. Common Preclinical drug response metrics Several common drug sensitivity metrics are used in DRP input data. All are obtained from sigmoidal dose-response curves that record cell viability (the activity of a specific biological process, or the number of cells) across a range of drug concentrations. Drug sensitivity is commonly determined relative to two reference values: control (i. e. cell viability in the absence of the drug) and maximal response (the maximally observed difference of cell viability, relative to control). Based on these two concepts, pharmacogenomic databases use a variety of metrics to quantify growth inhibition  [169, 170] , the most important being: • IC50 (inhibition concentration of 50%): the drug concentration at which cell viability is reduced to the half of control.
AA = activity area; AUC = area under curve; CDSS = custom drug sensitivity score; CDV = cross-dataset validation; CNV = copy number variation; DD = drug descriptors; DM = DNA methylation; ES = exome sequencing; MAGE = microarray gene expression; KFCV = K-fold cross validation; LOCO = leave-one-compound-out; LOOCV = leaveone-out cross validation; LOTO = leave-one-tissue-out; LPOCV = leave-pair-out cross validation; miRNA = micro-RNA; MS = microsatellite; Mu = mutation; OOBP = outof-bag prediction; PA = protein abundance; Res/Non-Res = responder/non-responder; RSGE = RNA-seq gene expression; RRSSCV = repeated random sub-sampling cross validation; Sen/Res = sensitive/resistance; SNP = single nucleotide polymorphism; TT = tissue type; TVC = tumor volume changes; V1 = viability at 1 μm.
org/content/3/12/1416. figures-o (FIMM) nly Genentech (GNE) IC50 675 >350 17 Klijn et al. (2015) https://www. nature. com/articles/ nbt. 3080#MOESM21 Human B-cell Cancer Cell AUC, Sen/Res 26 3 2 Falgreen et al. , https://www. ncbi. nlm. nih. gov/ Lines (HBCCL) (2015) geo/query/acc. cgi?acc=GSE53798 Cancer Therapeutics AUC 860 481 25 Seashore-Ludlow http://portals. broadinstitute. org/ Response Portal (CTRPv2) et al. (2015) ctrp. v2. 1/ Genentech Cell Line AUC, IC50 410 16 23 Haverty et al. https://pharmacodb. pmgenomics. Screening Initiative (2016) ca/datasets/4 (gCSI) Head and neck squamous IC50 8 276 1 Chia et al. (2017) https://www. chip-phenomics. org/ cell carcinomas cell lines DOWNLOAD/Chia2017/ Texas Southwestern AUC, ED50 100 222 2 McMillan et al. Medical Center non-small (2018) cell lung cancer cell line https://www.
(2016) GELnet LBNLBC MAGE GI50 LPOCV https://cran. r-project. o rg/web/packages/gelnet/i ndex. html Aben et al. (2016) TANDEM GDSC MAGE, Mu, IC50 KFCV https://cran. r-project. o CNV, DM, TT rg/web/packages/TANDE M/index. html Huang et al. (2020) TG-LASSO GDSC, TCGA MAGE IC50 RRSSCV, CDV https://github. com/ema d2/TG-LASSO Logistic Regression Geeleher et al. (2014) - GDSC, three clinical MAGE Res/Non-Res LOOCV, CDV http://geeleherlab. org/ datasets cgpPrediction/ Falgreen et al. (2015) - HBCCL, GDSC, IDRC, MAGE Sen/Res KFCV, LOOCV - LLMPP, MDFCI, UAMS Ding et al. (2016) - TCGA MAGE, CNV, Sen/Res - DM, miRNA Geeleher et al. (2017) - GDSC, TCGA MAGE IC50 KFCV, CDV https://osf. io/pwm4z/ Ding et al. (2018) - GDSC, CCLE MAGE, CNV, Mu Sen/Res RRSSCV - Huang et al. (2018) Lq-NLR GDSC, BATTLE MAGE IC50 KFCV - Maximum Margin Models Dong et al.
(2015) - GDSC, CCLE MAGE Sen/Res KFCV, CDV - Gupta et al. (2016) - CCLE MAGE, CNV, Mu IC50 - http://crdd. osdd. net/ra ghava/cancerdp/ Parca et al. (2019) - GDSC MAGE IC50 KFCV https://github. com/luca parca/dre Ensemble Learning Methods Riddick et al. (2010) - NCI-60 MAGE IC50 OOBP - Daemen et al. (2013) - LBNLBC, TCGA MAGE, DM, ES, Sen/Res RRSSCV, CDV - RSGE, PA, CNV Stetson et al. (2014) - NCI60, CCLE, GDSC MAGE, SNP, IC50 KFCV, CDV - CNV Wan & Pal (2014) - CCLE, NCI-DREAM MAGE, CNV, PA, GI50, IC50 KFCV, LOOCV - DM, RSGE Fang et al. (2018) QRF CCLE MAGE, Mu, AA OOBP - CNV Xu et al. (2019) AutoBorutaRF GDSC, CCLE MAGE, SNP, Sen/Res KFCV https://github. com/bioi CNV nformatics-xu/AutoBoru taRF Oskooei et al. (2019) NetBiTE GDSC MAGE IC50 KFCV - Su et al. (2019) Deep-Resp- GDSC, CCLE MAGE, CNV Sen/Res KFCV https://github.
com/Ra Forest nSuLab/Deep-Resp-Fore st Kurilov et al. (2020) - GDSC, CCLE, CTRP, gCSI, MAGE, CNV, IC50, AUC, V1, RRSSCV https://github. com/Roma NIBR PDXE Mu, RSGE, TT TVC HD/DrugRespPrediction Nearest Neighbour Method Li et al. (2021) GA/KNN GDSC, CCLE, TCGA, GTEx MAGE, RSGE IC50 RRSSCV, CDV - (Continued).
(2021) - GDSC, TCGA MAGE, Mu, IC50 KFCV, CDV https://github. com/Tea CNV, DM mSundar/BRCA_multio mics Molecular Network Similarity- Based Methods Kim et al. (2016) NBC GDSC, CCLE MAGE Sen/Res KFCV - Stanfield et al. (2017) - GDSC, CCLE SNP CDSS LOOCV, CDV - Multi-drug learning (MDL) models Linear Regression Costello et al. (2014) Bayesian NCI-DREAM, GDSC MAGE, RSGE, GI50 challenge test https://www. nature. co multitask MKL CNV, Mu, DM, cell lines m/articles/nbt. 2877#MOE PA SM3 Yuan et al. (2016) - CCLE, CTRPv2, NCI60 MAGE, CNV, Mu AA, AUC, GI50 KFCV - Ammad-ud-din et al. MVLR GDSC, FIMM MAGE IC50, CDSS LOOCV https://github. com/sulei (2017) mank/mvlr Ensemble Learning Methods Matlock et al. (2018) - GDSC, CCLE MAGE, DT, DD AUC RRSSCV - Liu et al. (2020) - GDSC, CCLE MAGE AA, AUC KFCV https://zenodo.
org/reco rd/1325121#. YNHB_i0Ro zV Sharma & Rani (2019) - GDSC, CCLE, NCI-Dream MAGE IC50, GI50 KFCV - Su et al. (2020) Meta-GDBP GDSC, CCLE MAGE, DD IC50, AA KFCV, CDV https://github. com/Ra nSuLab/Meta-GDBP Artificial Neural Networks and Deep Learning Menden et al. (2013) - GDSC MS, MU, CNV, IC50 KFCV - DD Chang et al. (2018) CDRscan GDSC, CCLP SNP, DD IC50 KFCV - Li et al. (2019) Deep DSC GDSC, CCLE MAGE, DD IC50 KFCV, LOTO, - LOCO Chiu et al. (2019) DeepDR GDSC, TCGA MAGE, Mu IC50 RRSSCV - Joo et al. (2019) DeepIC50 GDSC, CCLE, TCGA Mu, DD 3-class RRSSCV, CDV https://github. com/labna sensitivity ms/DeepIC50 Liu et al. (2019) tCNNS GDSC Mu, CNV, DD IC50 RRSSCV, LOTO https://github. com/Lo wpassfilter/tCNNS-Proje ct Manica et al. (2019) MCA GDSC MAGE, DD IC50 KFCV, RRSSCV https://github.
com/drugi lsberg/paccmann https://ibm. biz/paccma nn-aas Choi et al. (2020) RefDNN GDSC, CCLE MAGE, DD Sen/Res KFCV https://github. com/ma thcom/RefDNN Zhu et al. (2020) - GDSC, CCLE, CTRP, gCSI MAGE, DD AUC KFCC - Bazgir et al. (2020) REFINED GDSC, NCI-60 MAGE, DD IC50, GI50, KFCV, RRSSCV https://github. com/omi Sen/Res dbazgirTTU/REFINED Liu et al. (2020) DeepCDR GDSC, CCLE, TCGA MAGE, Mu, DM, IC50, Sen/Res KFCV, CDV https://github. com/ki DD mmo1019/DeepCDR (Continued).
org/reco rd/1403638#. YNHVyi0Ro zV Recommender Systems (model-based) Ammad-ud-din et al. KBMF GDSC MAGE, CNV, IC50 KFCV https://research. cs. aalto. (2014) Mu, DD, DT fi/pml/software/kbmf/ Ammad-ud-din et al. cwKBMF GDSC, CTRPv1, AMLCLP MAGE, DD IC50, AUC KFCV, CDV https://research. cs. aalto. (2016) fi/pml/software/ cwkbmf/ Wang et al. (2017) SRMF GDSC, CCLE MAGE, DD AA, IC50 KFCV https://github. com/linwa ng1982/SRMF Suphavilai et al. (2018) CaDRReS GDSC, CCLE, HNCCLP MAGE IC50 KFCV https://github. com/ CSB5/CaDRReS Yang et al. (2018) Macau GDSC MAGE, DT IC50 KFCV https://github. com/sae zlab/Macau_project_1 Guan et al. (2019) WGRMF GDSC, CCLE MAGE, DD IC50, AA KFCV - Moughari & Eslahchi ADRML GDSC, CCLE MAGE, Mu, IC50 KFCV https://github. com/fa (2020) CNV, DD, DT hmadimoughari/ADRML Emdadi & Eslahchi (2020) DSPLMF GDSC, CCLE MAGE, Mu, Sen/Res KFCV https://github.
com/emda CNV, DD di/DSPLMF Emdadi & Eslahchi (2021) Auto-HMM- GDSC, CCLE MAGE, CNV, Sen/Res KFCV https://github. com/emda LMF Mu, TT, DD di/Auto-HMM-LMF Network Representation Learning- Based Models Yang et al. (2019) NRL2DRP GDSC Mu, CNV, DM Sen/Res KFCV https://github. com/U STC-HIlab/NRL2DRP Yu et al. (2020) DREMO GDSC, CCLE MAGE, Mu, Sen/Res KFCV https://github. com/Lia CNV, DD, DT ngYu-Xidian/MNDRP Network Zhang et al. (2018) HNMDRP GDSC MAGE, DD Sen/Res LOOCV https://github. com/U Propagation- STC-HIlab/HNMDRP Based Method.
This means that, similarly to the yellow circle of Fig. 1b , the Pelagosa peak correctly exemplifies what an oceanic bottleneck represents: a very small portion of the ocean surface that permits the exchange and subsequent mixing of two water masses which occupied two, otherwise disconnected, larger oceanic regions. To prove the robustness of this structure to velocity fields of different origin and resolutions, we repeat the B L calculation varying the integration time τ and using also an altimetry-derived dataset (Methods). We find a remarkable regularity in the position of the peak and a consistent increase of its absolute value with τ , both for model and satellite observations (Supplementary Fig. 3 ). We also perform a temporal average of the B L field from the regional altimetry-derived velocity field across the years 2002-2013 (Supplementary Fig.
Plankton diversity and Lagrangian betweenness in the Kuroshio Front Lagrangian betweenness can also provide a framework for interpreting microbial community structure in fluid environments. We hypothesize that regions of high Lagrangian betweenness promote biodiversity by the confluence of upstream heterogeneous populations. Locally, fine scale dispersal processes intermingle them and subsequent divergence spreads the newly mixed population widely, seeding downstream environments. Here, we focus on the Kuroshio Extension Front, a highly dynamical region where the confluence of energetic currents with different proprieties has a strong influence on planktonic community composition  [19]. We examine the relationship of betweenness and diversity patterns in two independent datasets collected during a cruise in October 2009; microscopy-based abundances of plankton types and phylogenetically-derived abundances of Ostreococcus clades  [42, 43].
To locally quantify plankton diversity we calculate the species evenness  [44]  at each sampling site in both datasets (Methods). At the same time, we use velocities from a global, high-resolution ocean reanalysis product to compute Lagrangian betweenness fields during the sampling period (Methods). In Figure  8  (top panel) we show the cruise track and the betweenness field on 20 October 2009 with τ = 7 days. Two high betweenness strips are apparent and reflect the dominant eastward flow associated within the Kuroshio Extension Front. To allow a direct comparison with the estimated plankton diversity, we associate to each sampling location a value of betweenness equal to the average of the field in a circle of 0. 2 degrees of radius around the site (approximately twice the spacing between samples), calculated on the exact sampling time.
Moreover, since both Lagrangian Flow Networks diagnostics and Lyapunov exponents were proven to be robust to various input parameters and flow fields  [50, 51] , we expect that Lagrangian Betweenness will also share the same reliability across a wide range of oceanographic applications. Since high Lagrangian betweenness regions represent the optimal compromise between the number of water origins and destinations of potentially different hydrodynamical and biogeochemical properties, we tested the hypothesis that they could play a role in shaping diversity patterns of marine ecosystems  [18, 21, 52, 53]. In our Kuroshio Front application, despite the use of a reanalysis product not adapted to the region for the trajectory calculation, we find promising statistical correlations between Lagrangian betweenness fields and local plankton diversity measured from two independent observational datasets.
Plankton abundance data and evenness calculation The plankton data presented here was collected during a cruise to the Kursohio Extension Front in October 2009. The Kuroshio Extension is the eastward flowing extension of the Kuroshio western boundary current that is deflected off the coast of Japan. The Kuroshio Extension is characterized by strong zonal velocities and strong mesoscale and submesoscale and submesoscale dynamics. The goal of the cruise was to undertake a high spatial resolution physical-chemical-biological survey of the front to explore the connections between physical frontal processes and the plankton community at fine scales. The sampling strategy for the chemical and biological data collected during this cruise is fully described in  [42] , but here we briefly describe the data collection and samples used in this paper.
Five ship transects crossed the Kuroshio Extension Front, with 7-8 sampling stations spaced roughly 9km apart along each transect. At each sampling station, water was collected from the surface using a clean bucket and from 5 additional depths using Niskin bottles mounted to the CTD rosette. From each of these surface and depth samples, 500ml were collected and fixed with formaldehyde and then later used for microscopic enumeration of phytoplankton. The microscopy data is openly available from the Pangaea database  [66]. From the surface samples, 1000ml were collected for subsequent qPCR analysis of the abundance of Ostreococcus clades (method fully described in  [43] ). We locally estimate plankton diversity at each sampling site by calculating species evenness  [44]. For the microscopy dataset we focused only on surface samples and we identify species with different plankton types while for the Otreococcus dataset we consider the two clades as two species.
In both cases, we refer to the relative abundance of the species i as p i. Thus, for the sampling location j we calculate the species evenness E j as: E j = -i p i log(p i ) log (S j ) (32) where S j is the number of species in the location j. Note that very similar results are obtained using instead the Shannon index (i. e. the non-normalized evenness). However, we prefer evenness since it provides an absolute and normalized diversity measure that facilitate the comparison between different datasets. MAIN TEXT FIGURES A (x 0 ,t 0 ) (x 1 ,t 1 ) (x 2 ,t 2 ) (x 3 ,t 3 ) A a) b) FIG. 1. Betweenness concept in networks and dynamical systems. Panel (a) represents a small network composed by nodes (colored circles) and links (black segments). The central node A behaves as a bottleneck for the network since all the paths connecting the blue with the red part must go trough it.
ACKNOWLEDGEMENTS E. S-G thanks  Ricardo Martínez-García  for providing relevant feedback on the early versions of the manuscript. MJF and ES-R are very grateful for support from the  Simons Foundation : the Simons Collaboration on Computational BIOgeochemical modeling of  Marine EcosystemS  (CBIOMES # 549931  to MJF). C. L. and E. H-G acknowledge financial support from the  Spanish State Research Agency  through the  Maria de Maeztu Program for Units of Excellence in R&D  ( MDM-2017-0711 ).
DATA AVAILABILITY The data-assimilation product used for the Adriatic Sea are available at https://doi. org/10. 25423/MEDSEA REANALYSIS PHYS 006 004. The altimetry product used for the Adriatic Sea and Kerguelen region are available at https://www. aviso. altimetry. fr. The velocity field used in the north Pacific region is the GLORYS12V1 product (GLOBAL REANALYSIS PHY 001 030) provided by E. U. Copernicus Marine Environment Service Information website. The microscopy data are available at https://doi. org/10. 1594/PANGAEA. 819110. The sequences data used have been deposited in GenBank (accession nos. KT012724 to KT013052). The codes used to compute Lagrangian betweenness are available on-line at https://github. com/serjaaa/lagrangian-betweenness.
Neurotrophic tropomyosin receptor kinase (NTRK) gene fusions are rare oncogenic drivers in solid tumours. This study aimed to interrogate a large real-world database of comprehensive genomic profiling data to describe the genomic landscape and prevalence of NTRK gene fusions. NTRK fusion-positive tumours were identified from the FoundationCORE ® database of >295,000 cancer patients. We investigated the prevalence and concomitant genomic landscape of NTRK fusions, predicted patient ancestry and compared the FoundationCORE cohort with entrectinib clinical trial cohorts (ALKA-372-001 [EudraCT 2012-000148-88]; STARTRK-1 [NCT02097810]; STARTRK-2 [NCT02568267]). Overall NTRK fusion-positive tumour prevalence was 0. 30% among 45 cancers with 88 unique fusion partner pairs, of which 66% were previously unreported.
They were found in 0. 28% of cases and NTRK fusionpositive tumours were largely devoid of other oncogenic drivers. We aimed to expand these findings by analysing data from >295,000 cancer patients from the FoundationCORE ® database (Foundation Medicine Inc. , Cambridge, MA, USA) to investigate NTRK gene fusions prevalence, co-occurrence with relevant biomarkers/oncogenic drivers, associated fusion partners and cancer types/histologies. Additionally, NTRK fusion-positive cases in the FoundationCORE database were compared with those enrolled in three phase I/II entrectinib clinical trials  9  (ALKA-372-001 [EudraCT 2012-000148-88], STARTRK-1 [NCT02097810], STARTRK-2 [NCT02568267]), to determine if the study cohorts were representative of the real-world population.
Solid tumour NTRK gene fusion prevalence in the FoundationCORE database From 295,676 patients, NTRK gene fusions were found in 889 (prevalence = 0. 30%, Fig. 1a ). Demographics are presented in Table  1  and Supplementary Table  1. The 889 NTRK fusion-positive cases included 134 distinct histological subtypes from 45 cancer types (Supplementary Table  2 ). NTRK fusion-positive tumours prevalence varied by age and cancer type (Fig. 1a-e ); it was 0. 28% in adults (aged ≥18 years) and 1. 34% in paediatric patients (aged <18 years; Fig. 1a ). Prevalence increased with decreasing age, with children <5 years demonstrating the highest incidence of 2. 28% (Fig. 1b ; Supplementary Table  3 ); largely as a result of NTRK fusionpositive soft tissue fibrosarcoma (1. 06%, n = 13/1227 of all patients <5 years), not found in other age groups (Supplementary Table  4 ).
Spectrum of NTRK gene fusion partners detected in solid tumours Eighty-eight unique fusion partner pairs were identified, of which 65. 9% (n = 58/88) were not previously reported in other large public databases/studies  [4] [5] [6] 8, 9, 12, 13  (Fig. 2a  and b ; Supplementary Tables  6  and 7 ). ETV6:NTRK  was most common in adults (26. 4%, n = 78/295 [total cases with known fusion partners]) and paediatric patients (32. 7%, n = 17/52 [total cases with known fusion partners]). From all cases with known fusion partners, ETV6: NTRK3 (27. 2%, n = 95/349), TPM3:NTRK1 (21. 5%, n = 75/349) and LMNA:NTRK1 (9. 5%, n = 33/349) were the most common. The most common tumour type with ETV6 was salivary gland (36. 8%), with TPM3 was CRC (29. 3%) and with LMNA was CRC (39. 4%; Fig. 2c ).
Comparison of NTRK fusion-positive tumours in FoundationCORE with clinical trials Fifty-four adults with 11 different NTRK fusion-positive tumour types were enrolled into the entrectinib ALKA-372-001, STARTRK-1 and STARTRK-2 trials  9. Adult patients from the FoundationCORE database were matched to the 11 NTRK fusion-positive disease groups identified in trial patients. The frequency of patients with sarcoma, NSCLC, pancreatic, endometrial and cholangiocarcinoma cancers was similar in the clinical trial population and the FoundationCORE cohort (Fig. 5 ; Supplementary Table  14 ). The clinical trial population had a higher frequency of MASC, and the FoundationCORE population had a higher frequency of breast, CRC and ovarian cancers. Median age and sex distributions were similar in patients with NTRK fusion-positive tumours from the database and the clinical trials for the 11 matched tumour types (Supplementary Table  15 ).