Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors

A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text

The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and approaches in Natural Language Processing (NLP) based on English text are shown to be ineffective when attending to these language specific problems in late-started Chinese NLP. Relation Extraction is an area under NLP, looking to identify semantic relationships between entities in the text. The term “Effect Relation” is introduced in this research to refer to a specific content type of relationship between two entities, where one entity has a certain “effect” on the other entity. In this research project, a case study on Chinese text from Traditional Chinese Medicine (TCM) journal publications is built, to closely examine the forms of Effect Relation in this text domain. This case study targets the effect of a prescription or herb, in treatment of a disease, symptom or body part. A rule-based methodology is introduced in this thesis. It utilises predetermined rules and templates, derived from the characteristics and pattern observed in the dataset. This methodology achieves the F-score of 0.85 in its Named Entity Recognition (NER) module; 0.79 in its Semantic Relationship Extraction (SRE) module; and the overall performance of 0.46. A second methodology taking a feature-based approach is also introduced in this thesis. It views the RE task as a classification problem and utilises mathematical classification model and features consisting of contextual information and rules. It achieves the F-scores of: 0.73 (NER), 0.88 (SRE) and overall performance of 0.41. The role of functional words in the contemporary Chinese language and in relation to the ERs in this research is explored. Functional words have been found to be effective in detecting the complex structure ER entities as rules in the rule-based methodology

Epigenetics in Traditional Chinese Pharmacy: A Bioinformatic Study at Pharmacopoeia Scale

Epigenetics is a phenomenon of heritable changes in the chromatin structure of a genomic region, resulting in a transcriptional silent or active state of the region over cell mitosis. Mounting evidence has demonstrated phenotypic consequence of alternations in the patterns of DNA methylation and histone modifications, two of the well-studied epigenetic mechanisms. The epigenome thus represents an interesting therapeutic target. Traditional Chinese medicine (TCM) is a system of therapies that has developed through empiricism for over 2100 years and has remained a popular alternative medicine in some Far East Asian populations. We searched 3294 TCM medicinals (TCMMs) containing 48 491 chemicals for chemicals that interact with the epigenetics-related proteins and found that 29.8% of the TCMMs are epigenome- and miRNA-modulating via, mainly, interactions with Polycomb group and methyl CpG-binding proteins. We analyzed 200 government-approved TCM formulas (TCMFs) and found that a statistically significant proportion (99%) of them are epigenome- and miRNA-interacting. The epigenome and miRNA interactivity of the Monarch medicinals is found to be most prominent. Histone modifications are heavily exploited by the TCMFs, many of which are tonic. Furthermore, epigenetically, the Assistant medicinals least resemble the Monarch. We quantified the role of epigenetics in TCM prescription and found that epigenome- and miRNA-interaction information alone determined, to an extent of 20%, the clinical application areas of the TCMFs. Our results provide (i) a further support for the notion of the epigenomes as a drug target and (ii) a new set of tools for the design of TCM prescriptions

Identification of the anti-COVID-19 mechanism of action of Han-Shi Blocking Lung using network pharmacology-integrated molecular docking

Purpose: To investigate the bio-active components and the potential mechanism of the prescription remedy, Han-Shi blocking lung, with network pharmacology with a view to expanding its application. Methods: Chemical components were first collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Pharmmapper database and GeneCards were used to predict the targets related to active components and COVID-19. Using DAVIDE and KOBAS 3.0 databases, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched. A “components-targets-pathways” (C-T-P) network was conducted by Cytoscape 3.7.1 software. With the aid of Discovery Studio 2016 software, bio-active components were selected to dock with SARS-COV-2 3CL and ACE2. Results: From the prescription, 47 bio-active components, 83 targets and 103 signaling pathways were obtained in total (p < 0.05). 126 GO entries (p < 0.05) were screened by GO enrichment analysis. Molecular docking results showed that procyanidin B1 eriodictyol, (4E, 6E)-1, 7-bis(4- hydroxyphenyl)hepta-4, 6-dien-3-one, and quercetin had higher docking scores with SARS-COV-2 3CL and ACE2. Conclusion: With network pharmacology and molecular docking, the bio-active components and targets of this prescription, Han-Shi blocking lung, against COVID-19 were identified. Taken together, this study provided a basis for the treatment of COVID-19 and further promotion of this prescription

Using association rules mining to explore pattern of Chinese medicinal formulae (prescription) in treating and preventing breast cancer recurrence and metastasis.

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In silico approaches in the study of traditional Chinese herbal medicine

Ph.DDOCTOR OF PHILOSOPH

East Wind, West Wind: Toward the modernization of traditional Chinese medicine.

Traditional Chinese medicine (TCM) has used herbal remedies for more than 2,000 years. The use of complimentary therapies has increased dramatically during the last years, especially in the West, and the incorporation and modernization of TCM in current medical practice is gaining momentum. We reflect on the main bottlenecks in the modernization of arcane Chinese herbal medicine: lack of standardization, safety concerns and poor quality of clinical trials, as well as the ways these are being overcome. Progress in these areas will facilitate the implementation of an efficacy approach, in which only successful clinical trials lead to the molecular characterization of active compounds and their mechanism of action. Traditional pharmacological methodologies will produce novel leads and drugs, and we describe TCM successes such as the discovery of artemisinin as well as many others still in the pipeline. Neurodegenerative diseases, such as Parkinson's and Alzheimer's disease, cancer and cardiovascular disease are the main cause of mortality in the Western world and, with an increasing old population in South East Asia, this trend will also increase in the Far East. TCM has been used for long time for treating these diseases in China and other East Asian countries. However, the holistic nature of TCM requires a paradigm shift. By changing our way of thinking, from "one-target, one-drug" to "network-target, multiple-component-therapeutics," network pharmacology, together with other system biology methodologies, will pave the way toward TCM modernization

Network Pharmacology Approaches for Understanding Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.Perinteinen kiinalainen lääketiede (TCM) on ilmeinen tehokkuus taudin hoidoissa ja on arvokas lähde uuden lääkkeen löytämiseen. TCM: n farmakologisten vaikutusten taustalla oleva mekanismi pysyy kuitenkin tuntemattomassa, koska TCM on monimutkainen järjestelmä, jossa on useita yrttejä ja ainesosia, jotka tulevat yhteen reseptilääkkeeksi. Siksi on kiireellistä soveltaa Laskennallisia työkaluja TCM: lle ymmärtämään TCM-teorioiden taustalla oleva mekanismi molekyylitasolla ja käyttävät kehittyneitä verkkoalgoritmeja tutkimaan mahdollisia tehokkaita ainesosia ja havainnollistavat TCM: n periaatteita järjestelmän biologisissa näkökohdissa. Tässä opinnäytetyössä pyrimme ymmärtämään monimutkaisten TCM-järjestelmien toimintamekanismia molekyylitasolla bioinformaattilla ja laskennallisilla työkaluilla. Tutkimuksessa kehitettiin koneen oppimiskehystä yrttien ja ainesosien meridialaisista. Lopuksi saavutimme korkean tarkkuuden meridiaaneista yrtteistä ja ainesosista, mikä viittaa meridiaaneihin ja ainesosien ja yrtteihin liittyvien molekyylipiirin välillä, erityisesti koneen oppimismalleihin tärkeimmät ominaisuudet. Toiseksi ehdoimme uuden verkon lähestymistavan TCM-kaavojen tutkimiseksi kvantitoimisella vuorovaikutteisten yrttiparien vuorovaikutuksen tutkimuksessa ⅱ käyttämällä viisi verkkoetäisyyttä, mukaan lukien lähin, lyhyt, keskus, ydin sekä erottaminen. Osoitimme, että ylä-yrttiparien etäisyys on lyhyempi kuin satunnaisten yrttiparien, mikä viittaa voimakkaaseen vuorovaikutukseen ihmisellä vuorovaikutteisesti. Lisäksi Center-menetelmät ainesosan tasolla ylittivät muut menetelmät. Se vihjeitä meille, että keskeiset ainesosat ovat tärkeässä asemassa yrtteissä. Kolmanneksi tutkimme yrttien tai ainesosien välisiä yhdistyksiä ja niiden tärkeitä biologisia ominaisuuksia tutkimuksessa III, kuten ominaisuudet, meridiaanit, rakenteet tai tavoitteet klustereiden kautta moniparite-verkoston yhteisön analyysistä. Löysimme, että kasviperäiset lääkkeet samoilla klusterien keskuudessa ovat yleensä samankaltaisia ominaisuuksissa, meridiaaneissa. Samoin saman klusterin ainesosat ovat samankaltaisempia rakenteissa ja proteiinin tavoitteessa. Yhteenvetona tämä opinnäytetyö aikoo rakentaa silta TCM-järjestelmän ja nykyaikaisten lääkevalmisteiden välillä laskentatyökaluilla, mukaan lukien Meridian-teorian koneen oppimismalli, TCM-kaavojen verkkomallinnus sekä kasviperäiset lääkkeet ja niiden ainesosat Osoitimme, että uusien laskennallisten lähestymistapojen soveltaminen integroidulle korkean suorituskyvyttömiehille tarjosivat TCM: n näkemyksiä ja nopeuttaisivat romaanin huumeiden löytöä sekä toistuvat TCM: stä

Network Pharmacology and Traditional Chinese Medicine

Gynaecology & obstetric