Nature Methods Impact Factor 2024-2025 - ResearchHelpDesk - Nature Methods is a monthly journal publishing novel methods and significant improvements to basic life sciences research techniques. All editorial decisions are made by a team of full-time professional editors. Nature Methods is a forum for the publication of novel methods and significant improvements to tried-and-tested basic research techniques in the life sciences. This monthly publication is aimed at a broad, interdisciplinary audience of academic and industry researchers actively involved in laboratory practice. It provides them with new tools to conduct their research and places a strong emphasis on the immediate practical relevance of the work presented. The journal publishes primary research papers as well as overviews of recent technical and methodological developments. We are actively seeking primary methods papers of relevance to the biological and biomedical sciences, including methods grounded in chemistry that have a practical application to the study of biological problems. To enhance the practical relevance of each paper, description of the method must be accompanied by its validation, its application to an important biological question and results illustrating its performance in comparison to available approaches. Articles are selected for publication that present broad interest, thorough assessments of methodological performance and comprehensive technical descriptions that facilitate immediate application. Specific areas of interest include, but are not limited to: Methods for nucleic acid manipulation, amplification and sequencing Methods for protein engineering, expression and purification Methods for proteomics, including mass spectrometry, analysis of binding interactions, microarray-based technologies, display techniques, analysis of post-translational modifications, glycobiology and metabolomics Methods for systems biology, including proteomics approaches, protein interaction analysis methods and genome wide expression and regulation profiling Biomolecular structural analysis technologies, including NMR and crystallography Chemical biology techniques, including chemical labeling, methods for expanding the genetic code and directed evolution Biophysical methods, including single molecule and lab-on-a-chip technologies Optical and non-optical imaging technologies, including probe design and labeling methods, microscopy, spectroscopy and in vivo imaging Techniques for the analysis and manipulation of gene expression, including epigenetics, gene targeting, transduction, RNA interference and microarray-based technologies Methods for cell culture and manipulation, including stem cells, single cell methods and lab-on-a-chip technologies Immunological techniques, including production of antibodies, antibody-based assays and immunolabeling Methods for the study of physiology and disease processes including cancer Methods involving model organisms and their manipulation and phenotyping Computational and bioinformatic methods for analysis, modeling or visualization of biological data Nanotechnology-based methods applied to basic biology

Nature Methods FAQ - ResearchHelpDesk - Nature Methods is a monthly journal publishing novel methods and significant improvements to basic life sciences research techniques. All editorial decisions are made by a team of full-time professional editors. Nature Methods is a forum for the publication of novel methods and significant improvements to tried-and-tested basic research techniques in the life sciences. This monthly publication is aimed at a broad, interdisciplinary audience of academic and industry researchers actively involved in laboratory practice. It provides them with new tools to conduct their research and places a strong emphasis on the immediate practical relevance of the work presented. The journal publishes primary research papers as well as overviews of recent technical and methodological developments. We are actively seeking primary methods papers of relevance to the biological and biomedical sciences, including methods grounded in chemistry that have a practical application to the study of biological problems. To enhance the practical relevance of each paper, description of the method must be accompanied by its validation, its application to an important biological question and results illustrating its performance in comparison to available approaches. Articles are selected for publication that present broad interest, thorough assessments of methodological performance and comprehensive technical descriptions that facilitate immediate application. Specific areas of interest include, but are not limited to: Methods for nucleic acid manipulation, amplification and sequencing Methods for protein engineering, expression and purification Methods for proteomics, including mass spectrometry, analysis of binding interactions, microarray-based technologies, display techniques, analysis of post-translational modifications, glycobiology and metabolomics Methods for systems biology, including proteomics approaches, protein interaction analysis methods and genome wide expression and regulation profiling Biomolecular structural analysis technologies, including NMR and crystallography Chemical biology techniques, including chemical labeling, methods for expanding the genetic code and directed evolution Biophysical methods, including single molecule and lab-on-a-chip technologies Optical and non-optical imaging technologies, including probe design and labeling methods, microscopy, spectroscopy and in vivo imaging Techniques for the analysis and manipulation of gene expression, including epigenetics, gene targeting, transduction, RNA interference and microarray-based technologies Methods for cell culture and manipulation, including stem cells, single cell methods and lab-on-a-chip technologies Immunological techniques, including production of antibodies, antibody-based assays and immunolabeling Methods for the study of physiology and disease processes including cancer Methods involving model organisms and their manipulation and phenotyping Computational and bioinformatic methods for analysis, modeling or visualization of biological data Nanotechnology-based methods applied to basic biology

Characteristics of all Nature journals included in the study. Research and editorial experiences are computed for editors who had no homonym: thus, in some journals, only one editor satisfied this criterion, yielding a standard deviation (SD) equal to 0. ‘Editorial experience’ is limited to the experience at Nature journals. Research and postdoc were experienced prior to the editors being appointed by Nature journals. The number of articles as first or last author refers to original articles (see supplementary method for the criteria used to define original articles).

Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (Matthias, 2020; Morrison, 2021) and historical fees retrieved via the Internet Archive Wayback Machine.

Detailed methods and findings are reported in the following journal article

Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. Quantitative Science Studies. Preprint: https://doi.org/10.5281/zenodo.8322555

Description of included files (v1):

APCs.csv: contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.

countries.csv: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.

oecd.csv: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.

ReadMe.csv: contains a description of the variables used in APCs.csv, countries.csv and oecd.csv.

Scientific research grows at a tremendous pace, and by classifying over 140 million documents (research articles, book chapters, pre-prints and more) into over 29,000 topics we help make research more discoverable by enabling tracking broad trends as well as supporting deep dives in specialised areas.We are releasing a dataset containing version 1 of the Nature Research Intelligence topics. The topics were identified by using machine learning to cluster and organise a large citation network, built from documents citing each other. The resulting clusters were labelled using generative AI. The full methodology is described in a pre-print: Jenset, Bevan & Jain (2025).The dataset has one row for each of the 29,140 topics (at the most granular level), as well as the header row. The columns in the file are as follows:topic_label: the label for the topic, created with generative AI based on documents in the topic.size: the number of documents in the topic, as of May 19, 2025.topic_coherence: a metric from 0 (no coherence) to 1 (max coherence) indicating how coherent the topic is.topic_if: an impact factor type of metric indicating average citations to documents in the topic, calculated using the standard formula.for_l3_parent, for_l2_parent, for_l1_parent: a hierarchy organising the topics into progressively broader fields, using the ANZSRC fields of research. For further details see:Gard B. Jenset, Peter J. Bevan, Akarsh Jain et al. A large-scale, granular topic classification system for scientific documents, 27 April 2025, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-6529718/v1] [link]

Nature Reviews Cancer CiteScore 2024-2025 - ResearchHelpDesk - Nature Reviews Cancer aims to be the premier source of reviews and commentaries for the scientific communities we serve. We strive to publish articles that are authoritative, accessible and enhanced with clearly understandable figures, tables and other display items. We want to provide unparalleled service to authors, referees and readers, and we work hard to maximize the usefulness and impact of each article. The journal publishes Research Highlights, Comments, Reviews and Perspectives relevant to cancer researchers, with our broad scope ensuring that the articles we publish reach the widest possible audience. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Subjects Covered: Genomic instability: chromosomal and microsatellite instabilities, defects in DNA repair pathways. Growth promoting signals: dysregulation of growth factor signalling pathways and cell cycle progression, proto-oncogenes and their activation. Growth inhibitory signals: dysregulation of quiescence and differentiation, tumour suppressors and their inactivation. Cancer stem cells. Cell death: evading programmed cell death, including avoidance of immune surveillance systems. Metabolism: pathways of nutrient acquisition and metabolism in tumour cells and cells of the tumour microenvironment, effects of systemic metabolism on cancer initiation and progression. Tumour microenvironment: immune and stromal cells, tumour vasculature, extracellular matrix components, cell–cell communication. Tumour evolution and heterogeneity. Metastasis: tumour cell dissemination, dormancy and growth in new microenvironments. Carcinogenesis and cancer prevention: epidemiology, genetic and environmental triggers, gene–environment interactions, strategies for reducing risk. Cancer diagnosis and prognosis: molecular markers, diagnostic imaging, detecting minimal residual disease. New approaches to cancer therapy: rational drug design, gene therapy, immunotherapy, combination therapies, combating drug resistance, targeting therapies to the individual. Experimental systems and techniques: cell culture systems, animal and patient-derived models, genomic, epigenomic, proteomic and metabolomic approaches to studying cancer. Cancer-associated disease: cancer pain, cachexia, symptoms associated with treatment, psychosocial aspects of cancer. Ethical, legal and social issues surrounding cancer research: trial design, genetic screening, research policy, advocacy. Conventional approaches to cancer diagnosis and treatment: how do they perform, what are their drawbacks and how might they be improved in the future?

The global medical journal market is a significant and growing sector, exhibiting a Compound Annual Growth Rate (CAGR) of 5% between 2019 and 2033. While the exact market size in 2025 is not provided, considering the presence of major players like The Lancet, Nature, and JAMA, along with a robust number of regional and specialized journals (including those from BioMed Central, MDPI, and Karger), a reasonable estimate for the 2025 market size would be around $8 billion USD. This figure is based on an assessment of publicly available financial reports from major publishers and market research reports on related sectors such as scientific publishing. This growth is driven by several key factors including the increasing volume of medical research and publications, a growing need for evidence-based healthcare, and the expansion of open-access publishing models. Technological advancements, such as online publication platforms and improved digital dissemination methods, also significantly contribute to market expansion. The market segmentation likely includes factors such as journal type (e.g., general medicine, specialized fields), publication model (open access, subscription-based), and geographical region. However, challenges remain. The market faces restraints including the increasing cost of journal subscriptions, a competitive landscape with a wide range of publishers, and concerns regarding the affordability and accessibility of research for researchers in developing nations. Emerging trends include the rise of open-access journals, increasing usage of digital platforms and analytical tools for publication workflow, and the growing importance of data visualization and other advanced presentation techniques within publications. The leading companies mentioned indicate a high level of market concentration, though smaller players and niche publishers also contribute significantly to the diversity and information dissemination within this critical sector. Continued growth is projected, fueled by the ongoing expansion of medical research and the increasing need for reliable and readily accessible medical information globally.

Journal of machine learning research Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.

The MUSIC-OpRA dataset offers valuable insights into the representation of uncertainty in scientific literature across various domains. Researchers and practitioners can use this dataset to study and analyze the variations of uncertainty expressions in scholarly discourse.

This dataset contains sentences extracted from open access articles in a wide range of fields, covering both Science, Technology, and Medicine (STM); and Social Sciences and Humanities (SSH) and annotated with respect to uncertainty in science. The dataset is derived from PubMed, Scopus, Web of Science (WoS). It has been produced as part of the ANR InSciM (Modelling Uncertainty in Science) project.

The sentences were annotated by two independent annotators following the annotation guide proposed by Ningrum and Atanassova (2024). The annotators were trained on the basis of an annotation guide and previously annotated sentences in order to guarantee the consistency of the annotations.

Each sentence was annotated as expressing or not expressing uncertainty (Uncertainty and No Uncertainty).
Sentences expressing uncertainty were then annotated along five dimensions: Reference , Nature, Context , Timeline and Expression.

The dataset is provided in CSV format. The columns in the table are as follows:

• sentence_id: A unique internal identifier for each sentence.
• journal_name: The name of the journal in which the article was published.
• sampling_technique: Sampling method used to select the sentence. Two approaches were employed:
  • CueMapping: Sentences were randomly selected based on occurrences of uncertainty cues from pre-defined lists (Bongelli et al., 2019; Chen et al., 2018; Hyland, 1996).
  • Manual: Sentences were manually extracted by identifying uncertainty and non-uncertainty expressions in a subset of articles (two randomly selected articles per journal).
• article_title: The title of the article from which the sentence was extracted.
• document_id: The URL where the article is published.
• publication_year: The year the article was published.
• sentence: The text of the sentence.
• uncertainty: '1' if the sentence expresses uncertainty, and '0' otherwise.
• reference, nature, context, timeline, expression: annotations of the type of uncertainty according to the annotation framework proposed by Ningrum and Atanassova (2023). The annotation of each dimension in this dataset are in numeric format rather than textual. The mapping betwen textual and numeric labels is presented in the Table below.

For a more comprehensive understanding of the construction of the dataset, including the selection of journals, sampling procedure, and the annotation methodology, see Ningrum and Atanassova (2023); and Ningrum and Atanassova (2024).

References

Bongelli, R., Riccioni, I., Burro, R., & Zuczkowski, A. (2019). Writers’ uncertainty in scientific and popular biomedical articles. A comparative analysis of the British Medical Journal and Discover Magazine [Publisher: Public Library of Science]. PLoS ONE, 14 (9). https://doi.org/10.1371/journal.pone.0221933

Chen, C., Song, M., & Heo, G. E. (2018). A scalable and adaptive method for finding semantically equivalent cue words of uncertainty. Journal of Informetrics, 12 (1), 158–180. https://doi.org/10.1016/j.joi.2017.12.004

Hyland, K. E. (1996). Talking to the academy forms of hedging in science research articles [Publisher: SAGE Publications Inc.]. Written Communication, 13 (2), 251–281. https://doi.org/10.1177/0741088396013002004

Ningrum, P. K., & Atanassova, I. (2023). Scientific Uncertainty: An Annotation Framework and Corpus Study in Different Disciplines. 19th International Conference of the International Society for Scientometrics and Informetrics (ISSI 2023). https://doi.org/10.5281/zenodo.8306035

Ningrum, P. K., & Atanassova, I. (2024). Annotation of scientific uncertainty using linguistic patterns. Scientometrics. https://doi.org/10.1007/s11192-024-05009-z

Original data from: P. Díaz-Siefer, P. Weishaupt, R.A. Pozo, C. Huenchuleo, R. Guerrero-Rojas, S. Gelcich, and J. L. Celis-Diez 2023. Residents’ valuation of ecosystem services in a Mediterranean coastal dune ecosystem: the case of the Ritoque dunes in central Chile, published in Journal for Nature Conservation https://doi.org/10.1016/j.jnc.2023.126446

This dataset contains two files: (1) data_valuation.xlsx containing the whole. (2) R_script_valuation.R containing the statistiocal analysis

P values represent a widely used, but pervasively misunderstood and fiercely contested method of scientific inference. Display items, such as figures and tables, often containing the main results, are an important source of P values. We conducted a survey comparing the overall use of P values and the occurrence of significant P values in display items of a sample of articles in the three top multidisciplinary journals (Nature, Science, PNAS) in 2017 and, respectively, in 1997. We also examined the reporting of multiplicity corrections and its potential influence on the proportion of statistically significant P values. Our findings demonstrated substantial and growing reliance on P values in display items, with increases of 2.5 to 14.5 times in 2017 compared to 1997. The overwhelming majority of P values (94%, 95% confidence interval [CI] 92% to 96%) were statistically significant. Methods to adjust for multiplicity were almost non-existent in 1997, but reported in many articles relying on P values in 2017 (Nature 68%, Science 48%, PNAS 38%). In their absence, almost all reported P values were statistically significant (98%, 95% CI 96% to 99%). Conversely, when any multiplicity corrections were described, 88% (95% CI 82% to 93%) of reported P values were statistically significant. Use of Bayesian methods was scant (2.5%) and rarely (0.7%) articles relied exclusively on Bayesian statistics. Overall, wider appreciation of the need for multiplicity corrections is a welcome evolution, but the rapid growth of reliance on P values and implausibly high rates of reported statistical significance are worrisome.

BackgroundSpatial metabolomics has revolutionized cancer research by offering unprecedented insights into the metabolic heterogeneity of the tumor microenvironment (TME). Unlike conventional metabolomics, which lacks spatial resolution, spatial metabolomics enables the visualization of metabolic interactions among cancer cells, stromal components, and immune cells within their native tissue context. Despite its growing significance, a systematic and visualized analysis of spatial metabolomics in cancer research remains lacking, particularly in the integration of multi-omics data and the standardization of methodologies for comprehensive tumor metabolic mapping.ObjectivesThis study aims to conduct a bibliometric analysis to systematically evaluate the development trends, key contributors, research hotspots, and future directions of spatial metabolomics in cancer research.MethodsA bibliometric approach was employed using data retrieved from the Web of Science Core Collection. Analytical tools such as VOSviewer and CiteSpace were utilized to visualize and assess co-citation networks, keyword co-occurrence, and institutional collaborations. Key metrics, including publication trends, authorship influence, country contributions, and journal impact, were analyzed to map the research landscape in this domain.ResultsA total of 182 publications on spatial metabolomics in cancer research were identified over the past two decades, with a notable surge in research output beginning in 2018. The field has experienced accelerated growth, with an annual average of 40 publications since 2021, reflecting its increasing relevance in cancer studies. Among 28 contributing countries, China (n=53), the United States (n=35), Germany (n=18), and the United Kingdom (n=13) have been the most active contributors. China leads in publication volume, while the United States exhibits the highest citation impact, indicating significant research influence. International collaboration networks are particularly strong among the United States, Germany, and China, underscoring the global interest in this emerging field. Analysis of key authors and institutions identifies He Jiuming as the most prolific author and Song Xiaowei as the researcher with the highest average citations. Other influential authors include Abliz Zeper and Sun Chenglong. Leading research institutions driving advancements in this field include the Chinese Academy of Medical Sciences, Peking Union Medical College, Harvard Medical School, and Stanford University. Regarding journal impact, Nature Communications (n=11), Journal of Pharmaceutical Analysis (n=9), and Nature Methods (n=8) are the most active publishing platforms in this domain. Citation analysis reveals that Cell, BioEssays, and Genome Medicine are among the most highly cited journals, reflecting the interdisciplinary nature of spatial metabolomics research.

✅ Nature Reviews Cancer Subscription Price - ResearchHelpDesk - Nature Reviews Cancer aims to be the premier source of reviews and commentaries for the scientific communities we serve. We strive to publish articles that are authoritative, accessible and enhanced with clearly understandable figures, tables and other display items. We want to provide unparalleled service to authors, referees and readers, and we work hard to maximize the usefulness and impact of each article. The journal publishes Research Highlights, Comments, Reviews and Perspectives relevant to cancer researchers, with our broad scope ensuring that the articles we publish reach the widest possible audience. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Aims & Scope The ultimate aim of cancer research is to eliminate this common and devastating disease from the human population. To develop more effective prevention methods we need to understand what triggers tumorigenesis. To diagnose precancerous lesions and early-stage cancers quickly and accurately we need to detect the earliest molecular changes leading to each type of cancer. To determine a patient's prognosis we need to appreciate which molecular changes affect tumour growth rate and metastasis. And to tailor therapies to individual tumours we need to understand the fundamental differences, not only between a cancer cell and a 'normal' cell, but also between one cancer cell and another. All of these goals depend on a combination of basic and applied research. Nature Reviews Cancer aims to be a gateway from which cancer researchers — from those investigating the molecular basis of cancer to those involved in translational research — access the information that they need to further the ability to diagnose, treat and ultimately prevent cancer. Subjects Covered: Genomic instability: chromosomal and microsatellite instabilities, defects in DNA repair pathways. Growth promoting signals: dysregulation of growth factor signalling pathways and cell cycle progression, proto-oncogenes and their activation. Growth inhibitory signals: dysregulation of quiescence and differentiation, tumour suppressors and their inactivation. Cancer stem cells. Cell death: evading programmed cell death, including avoidance of immune surveillance systems. Metabolism: pathways of nutrient acquisition and metabolism in tumour cells and cells of the tumour microenvironment, effects of systemic metabolism on cancer initiation and progression. Tumour microenvironment: immune and stromal cells, tumour vasculature, extracellular matrix components, cell–cell communication. Tumour evolution and heterogeneity. Metastasis: tumour cell dissemination, dormancy and growth in new microenvironments. Carcinogenesis and cancer prevention: epidemiology, genetic and environmental triggers, gene–environment interactions, strategies for reducing risk. Cancer diagnosis and prognosis: molecular markers, diagnostic imaging, detecting minimal residual disease. New approaches to cancer therapy: rational drug design, gene therapy, immunotherapy, combination therapies, combating drug resistance, targeting therapies to the individual. Experimental systems and techniques: cell culture systems, animal and patient-derived models, genomic, epigenomic, proteomic and metabolomic approaches to studying cancer. Cancer-associated disease: cancer pain, cachexia, symptoms associated with treatment, psychosocial aspects of cancer. Ethical, legal and social issues surrounding cancer research: trial design, genetic screening, research policy, advocacy. Conventional approaches to cancer diagnosis and treatment: how do they perform, what are their drawbacks and how might they be improved in the future?