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 Reviews Cancer Impact Factor 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?

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]

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.