José Liñares-Blanco

749 citations
10 papers · 419 indexed · 1 hit paper · h-index 6

José Liñares-Blanco

9 papers receiving 407 citations

Hit Papers

A review on machine learning approaches and trends in dru...264202120262022202450100150200250

Peers

José Liñares-Blanco
Comparison fields: 5 of 93
  • Computational Theory and Mathematics 199
  • Health Informatics 11
  • Molecular Biology 214
  • Pharmacology 24
  • Health Information Management 12
Replace Giovanni Bocci with:
Giovanni Bocci United States
Yoonjeong Cha Israel
Jihui Zhao China
Heval Ataş Türkiye
Hanbin Shan China
Liang‐Chin Huang United States
Sangsoo Lim South Korea
Richard Zang United States
Ziaurrehman Tanoli Finland
Kristina Preuer Austria
José Liñares-Blanco relative to Giovanni Bocci United States Giovanni Bocci's profile →
Citations per field
00.5×1.5×2.4×
Giovanni Bocci · 1×
Citations per year

Countries citing papers authored by José Liñares-Blanco

Since Specialization
Citations

This map shows the geographic impact of José Liñares-Blanco's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by José Liñares-Blanco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José Liñares-Blanco more than expected).

Fields of papers citing papers by José Liñares-Blanco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José Liñares-Blanco. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by José Liñares-Blanco. The network helps show where José Liñares-Blanco may publish in the future.

Co-authorship network

The 18 scholars most cited alongside José Liñares-Blanco, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with José Liñares-Blanco Line = papers co-authored together José Liñares-Blanco links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 20250
2 20251
3 20231
4 202218
5
A review on machine learning approaches and trends in drug discoverybreakdown →
2021264
6 202126
7 202131
8 202026
9 20201
10 201851

About José Liñares-Blanco

José Liñares-Blanco is a scholar working on Computational Theory and Mathematics, Microbiology and Molecular Biology, having authored 10 papers that have together received 419 indexed citations. Recurring topics across this work include Gut microbiota and health (5 papers), Computational Drug Discovery Methods (3 papers), vaccines and immunoinformatics approaches (1 paper), Advanced Breast Cancer Therapies (1 paper), Machine Learning in Bioinformatics (1 paper), Diabetes and associated disorders (1 paper), Cardiac Fibrosis and Remodeling (1 paper) and Metabolomics and Mass Spectrometry Studies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (199 citations), Health Informatics (11 citations) and Molecular Biology (214 citations). José Liñares-Blanco has collaborated with scholars based in Spain, United Kingdom and Germany. Frequent co-authors include Carlos Fernández-Lozano, Alejandro Pazos, Adrián Carballal, Víctor Maojo, Nereida Rodríguez-Fernández, Francisco Cedrón, Francisco J. Nóvoa, Ana B. Porto-Pazos, Cristian R. Munteanu and Guillermo López–Campos. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Scientific Reports.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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