All publications sorted by journal and type
| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-450 | 451-475 | 476-500 | 501-525 | 526-528 |
BMC Bioinformatics
| , , and , Construction of an annotated corpus to support biomedical information extraction (2009), in: BMC Bioinformatics, 10(349) |
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| , , , and , Investigating heterogeneous protein annotations toward cross-corpora utilization (2009), in: BMC Bioinformatics, 10(403) |
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| , and , Accelerating the annotation of sparse named entities by dynamic sentence selection (2008), in: BMC Bioinformatics, 9:Suppl 11(S8) |
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| , and , Corpus annotation for mining biomedical events from literature (2008), in: BMC Bioinformatics, 9:1(10) |
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| and , Distinguishing the species of biomedical named entities for term identification (2008), in: BMC Bioinformatics, 9:Suppl 11(S6) |
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| , , and , How to make the most of NE dictionaries in statistical NER (2008), in: BMC Bioinformatics, 9:Suppl 11(S5) |
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| , , , , , and , New challenges for text mining: Mapping between text and manually curated pathways (2008), in: BMC Bioinformatics, 9:Suppl 3(S5) |
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| , and , Normalizing biomedical terms by minimizing ambiguity and variability (2008), in: BMC Bioinformatics, 9:Suppl 3(S2) |
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| , and , Mining protein function from text using term-based support vector machines (2005), in: BMC Bioinformatics, 6:Suppl 1(S22) |
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BMC Medical Informatics and Decision Making
| , , and , Improving reference prioritisation with PICO recognition (2019), in: BMC Medical Informatics and Decision Making, 19(256) |
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| , , , , and , Identification of Research Hypotheses and New Knowledge from Scientific Literature (2018), in: BMC Medical Informatics and Decision Making, 18(46) |
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| , , and , Using text mining techniques to extract phenotypic information from the PhenoCHF corpus (2015), in: BMC Medical Informatics and Decision Making, 15:Suppl. 2(S3) |
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| , and , A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data (2013), in: BMC Medical Informatics and Decision Making, 13:Suppl 1(S6) |
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| , and , ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials (2012), in: BMC Medical Informatics and Decision Making, 12:Suppl 1(S3) |
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BMJ Open
| , , , , , and , Youth Culturally adapted Manual Assisted Problem Solving Training (YCMAP) in Pakistani adolescent with a history of self harm: Protocol for multi-centre clinical and cost effectiveness randomised controlled trial (2022), in: BMJ Open, 12(e056301) |
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| , , , , and , Women’s health in The BMJ: a data science history (2020), in: BMJ Open, 10(e039759) |
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Briefings in Bioinformatics
| , , and , Text mining and ontologies in biomedicine: making sense of raw text. (2005), in: Briefings in Bioinformatics, 6:3(239--251) |
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Briefings in Functional Genomics
| , , , and , Event Based Text Mining for Biology and Functional Genomics (2014), in: Briefings in Functional Genomics, 14:3(213-230) |
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Clinical Science
| , , , , , , , , , , and , Risk of bias reporting in the recent animal focal cerebral ischaemia literature (2017), in: Clinical Science, 131:20(2525--2532) |
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Computatational Intelligence
| , , and , Discovering Robust Embeddings in (Dis)Similarity Space for High-Dimensional Lingustic Features (2012), in: Computatational Intelligence |
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Computational Intelligence
| , , , and , Bio-Molecular Event Extraction with Markov Logic (2011), in: Computational Intelligence, 27:4(558-582) |
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| , , , and , Extracting Bio-Molecular Events From Literature—The BioNLP’09 Shared Task (2011), in: Computational Intelligence, 27:4(513-540) |
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| , and , Extracting Secondary Bio-Event Arguments with Extraction Constraints (2011), in: Computational Intelligence, 27:4(702-721) |
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Computational Linguistics
| , Natural Language Processing and Computational Linguistics (2021), in: Computational Linguistics, 47:4(707-727) |
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| and , Feature Forest Models for Probabilistic HPSG Parsing (2008), in: Computational Linguistics, 34:1(35–80) |
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| 1-25 | 26-50 | 51-75 | 76-100 | 101-125 | 126-150 | 151-175 | 176-200 | 201-225 | 226-250 | 251-275 | 276-300 | 301-325 | 326-350 | 351-375 | 376-400 | 401-425 | 426-450 | 451-475 | 476-500 | 501-525 | 526-528 |