iMed User Manual
Nhanganyaya
1.1. Chinangwa
Chinangwa cheizvi web Kushandisa kutora ruzivo rwakadzama uye kubvumira kurwushandisa nenzira inopa mhedzisiro inobatsira mukuita sarudzo. Izvi zvinogona kuva kudzidzisa modhi ine data rakasvibirira kana kufanotaura mhedzisiro uchishandisa modhi uye ongororo.
1.2. Navigational Menu
Menyu yekufambisa iri pamusoro pepeji inobata zvese zvinongedzo kuti usvike kwaunoda kuve. Kana ukamborasika, unogona kudzvanya museve wekumashure kuti usvike papeji yaunoziva, dzokera kumba, kana kuwana peji rauri kutsvaga mukati memenu yekufambisa.
1.3. Akaundi
Kana iwe usati watova neakaundi, unofanirwa kunyoresa kuti ushandise iyo application. Kuti uite kudaro, tinya bhatani reakaundi kumusoro kurudyi uye tinya regista. Wobva waisa zita rako rekushandisa, password, uye email kuti uenderere mberi.
Kana iwe uchitova neakaundi, pinda uchishandisa zita rako rekushandisa uye password.
Peji Yekumusha
Nekudzvanya pazvinhu zviri kuruboshwe rwepeji, tsananguro yechimwe nechimwe ichaonekwa pakati pepeji kuti ikubatsire kunzwisisa zvinoita chimwe nechimwe.
iMedBot
Iyo iMedBot application inopa interface inosimudzira nyore mushandisi kutaurirana nevamiririri, inogonesa kufanotaura kwemunhu uye modhi yekudzidziswa. Inoshanda sedanho rekutanga rekushandura zvakabuda mukutsvagisa kwekudzidza kwakadzama kuita chishandiso chepamhepo, icho chine mukana wekumutsa zvimwe zvekutsvaga mudura iri. Bhuku rayo rekushandisa rinogona kuwanikwa pano.
Data Analysis
4.1. Nzvera Subsets
Ichi chikamu chinobvumira mushandisi kugadzirisa yavo dataset. Iwe unogona kusarudza kurodha dataset nyowani kana kushandisa iripo kubva pane yekudonha-pasi menyu.
Kana dataset yaiswa, unogona kusarudza kuti ndechipi chiito chaungade kutora nekudzvanya imwe yesarudzo iri kuruboshwe kuruboshwe menyu.
4.1.1. Nzvera Subsets Kubva pane Mafirita
Ichi chikamu chinobvumira kuwana diki diki rekutanga dataset zvichienderana nemasefa akapihwa. Sarudza maitiro aunoda mune subset uye wozosarudza makoramu aunoda kuratidzwa mune yekupedzisira dataset.
4.1.2. Dzokera Zvakarongwa
Izvi zvinodzosa iyo dataset mune yakarongwa fomu. Sarudza koramu yaunonangwa, kurongeka, nhamba yemitsara yekudzoka, uye kuti ndeapi makoramu ekuratidza mukubuda kwekupedzisira.
4.1.3. Wedzera iyo Dataset
Izvi zvinobvumira mushandisi kuti awedzere koramu imwe yakachengetwa seduramazwi kuita tafura chaiyo iyo mushandisi anogona kushandura. Zvinotora nested dataset uye inofambisa izvo zvinodikanwa nemushandisi mune yepamusoro-yakanyanya layer. Chekutanga, rodha dataset inosanganisira column ine nested dataset. Kana koramu inoda kukwidziridzwa ikaonekwa yega, sarudza kuti ndeipi kolamu yekuwedzera uye kuti ndeapi makoramu ekutora kubva muruzivo rwakaiswa. Dzvanya tumira uye unogona view ruzivo rwako semakoramu etafura pane nested data.
4.2. Batanidza Files
Nekusarudza uye kurodha akawanda dataset nekudzvanya ctrl (command for mac), izvi zvinovabatanidza kuita rimwe hombe dataset pane kushandiswa kune chimwe chinhu.
Ingosarudza ese dataset uye zadza ruzivo rwunodiwa. Izvi zvichachengetedza dataset nyowani kune iMed application uye yobva yawanikwa yekurodha.
4.3. Plot Mabasa
Ichi chikamu chinobvumira mushandisi kuronga dataset yavo. Sarudza imwe yesarudzo iri kuruboshwe-kuruboshwe menyu uye wozozadza minda inodiwa kuti utore chikamu chako. Pazasi pane marudzi ezvirongwa zvaunogona kugadzira kubva kune yako data:
4.4. Statistical Analysis
Ichi chikamu chinoita kuti tiite bvunzo dzenhamba pane yedu dataset. Sarudza bvunzo yekumhanya kubva kuruboshwe kuruboshwe menyu uye zadza minda kuti uite bvunzo. Pazasi pane mhando dzebvunzo dziripo:
ODPAC
5.1. Dzidza
Iri peji rinosanganisira tsananguro pfupi yerudzi rwega rwega rwezvekushandisa rinowanikwa pane ino peji. Kudzvanya bhatani riri pamusoro pechikamu chega chega kuchabatanidza kune imwe peji inobvumira mushandisi kushandisa kana kudzidza zvakawanda nezvemusoro wenyaya.
5.1.1. Epistasis
Peji ino inoita kuti tishandise MBS, algorithm yekutsvaga kudzidza kubva kune data. Kunyanya, inotibvumira kudzidza epistasis, kuwirirana pakati pemajini maviri kana anopfuura anokanganisa phenotype. Izvi zvinobatsira kune profile zvirwere zve genetic. Nzira dzakajairwa hadzina kukodzera kubata data repamusoro-soro rinowanikwa mune genome-wide association studies (GWAS). Iyo Multiple Beam Search (MBS) algorithm inobvumira kuona ari kupindirana majini nekukurumidza zvakanyanya. Isa iyo data yaunoda kushandisa uye wozoisa iyo inodiwa minda. Kuti uwane rumwe ruzivo rwakadzama, tsvaga bepa rakazara pano.
5.1.2. Risk Factors
Iri peji rinotibvumira kushandisa iyo IGain package kuti tidzidze kudyidzana pakati pe data. Iyo inonyatso dzidza kudyidzana kubva kumusoro-dimensional data uchishandisa heuristic yekutsvaga. Iyi nzira inovaka paExhaustive_IGain nzira yakambogadzirwa kuti udzidze kupindirana kubva kune yakaderera-dimensional data. Isa iyo data uye wozoisa iyo inodiwa minda. Rumwe ruzivo nezve IS zvikumbaridzo uye iGain inogona kuwanikwa pano.
5.1.3. Prediction Models
Ichi chikamu chinobvumira kushandiswa kwemamodhi ekufungidzira akatovakwa kare pamusoro pemashini ekudzidza modhi kuti ikurumidze kushandiswa kwayo. Izvi zvinobvumira kushandiswa kwavo pasina kushandiswa kwekodhi uye ruzivo rwekare kufanotaura mamodheru vachishandisa yavo dataset. Kune akawanda ekufanotaura modhi anowanikwa kumushandisi anosanganisira Logistic, Regression, Tsigiro Vector Machines (SVMs), Sarudzo Miti, uye zvimwe zvakawanda. Rondedzero yakazara yemaitiro ekufanotaura anowanikwa kurudyi rwepeji pano.
5.2. Kufanotaura
Ichi chikamu chinobvumira fungidziro kubva kune yakagovaniswa modhi yakamboiswa. Kutanga kurodha modhi yakagovaniswa kana isati yaitwa. Wobva wasarudza iyo modhi yekushandisa kufanotaura nekudzvanya zita remuenzaniso. Wobva waisa iyo data yekufembera modhi yekushandisa. Izvi zvinogona kuitwa nemaoko uchishandisa fomu riri pazasi peji kana kushandisa template iripo yekurodha. Kana uchishandisa template, rodha iyo dataset file uye tinya tumira kuti ugamuchire fungidziro yemuenzaniso.
5.3. Sarudzo Tsigiro
Tsigiro yesarudzo inopa kupatsanura uye inogona kutungamira sarudzo dzekurapa kubva kune ruzivo rwunopihwa kune sisitimu. Yakave yakadzidziswa kubva kune data kuti ikurudzire nzira yekurapa yakakwana zvichienderana nemurwere. Rumwe ruzivo nezve Clinical Decision Support Systems (CDSS) inogona kuwanikwa pano.
Kurudziro yeSystem inotora zvimiro zvemurwere uye inokurudzira nzira yekurapa uye inofanotaura mukana wenguva yemberi wemakore mashanu metastasis. Kupindira Kwemushandisi kunotora zvese zviri zviviri zvemurwere uye nzira yekurapa kufanotaura mukana wenguva yemberi ye5 gore metastasis zvichienderana nekurapa kwazvino pane kurapwa kwakaringana.
MBIL
Iyo Markov Blanket uye Interactive Risk Factor Mudzidzi (MBIL) is algorithm inodzidza imwechete uye inopindirana njodzi zvinhu zvine pesvedzero yakananga pane mhedzisiro yemurwere. Dzvanya "enda kuMBIL" kuti uendeswe kuPython Package Index (PyPI) yeMBIL package iri pano. Rumwe ruzivo nezve MBIL runogona kuwanikwa paBMC Bioinformatics.
Datasets
Ichi chikamu chinobvumira mushandisi kuona uye kurodha datasets matsva kune web application.
7.1. Ona Yese Dataset Inowanikwa
Kuti uone ese madatasets aripo, ingobaya "Ratidza Available Datasets."
7.2. Isa Dataset
Kuti uise dataset, tinya "Goverana Madhata Ako" uye wozozadza ruzivo rwunodiwa sezvakataurwa pa webpeji. Kutanga, isa dataset uye uzadze minda inodiwa.
Wobva wazadza minda iri pazasi kana kurodha chinyorwa file nemashoko akazadzwa. An example yekuronga ruzivo kuti application inzwisise yakapihwa pazasi.
Models
Ichi chikamu chinobvumira mushandisi kuona mhando dziripo kwavari uye kugovera modhi.
8.1. Ona Mienzaniso Yese Inowanikwa
Kuti uone mhando dzese dziripo, tinya "Ratidza Mamodheru Anowanikwa."
8.2. Govera Muenzaniso
Kuti ugovane modhi, tinya pakanzi “Goverana Mamodheru Ako” wobva waisa modhi file kudzidziswa ne tensor flow kana PyTorch.
8.2.1. Related Dataset
Iwe unofanirwa kurodha iyo inoenderana dataset iyo inosanganisira misoro. Kirasi/label yedataset inofanira kunge iri mukoramu yekupedzisira.
8.2.2. Predictors uye Kirasi ruzivo
Kana iyo dataset ichisanganisira ese maficha, iyo fomu yechimiro inogona kusvetuka mushure mekuisa iyo dataset. Nekudaro, kana dzisiri dzese dzakabatanidzwa, ruzivo urwu runofanira kupihwa murondedzero file kana mukati mechimiro chechimiro. Sarudza sarudzo kubva pakudonhedza pasi ichiratidza kuti urikuda kupa sei vanofanotaura uye ruzivo rwekirasi.
Kana uchishandisa sarudzo yekutsanangura, unogona kuzadza minda kana kurodha zvinyorwa file nemashoko akazadzwa. An example yekuronga ruzivo rwakapihwa pazasi.
Zvinyorwa / Zvishandiso
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Zvishandiso iMed Web Application [pdf] User Manual iMed, iMed Web Application, Web Application |