{"id":6815,"date":"2020-02-10T15:35:21","date_gmt":"2020-02-10T14:35:21","guid":{"rendered":"http:\/\/semmelweis.hu\/bioinformatika\/?page_id=6815"},"modified":"2024-11-15T14:06:48","modified_gmt":"2024-11-15T13:06:48","slug":"a-targy-reszletes-tematikaja","status":"publish","type":"page","link":"https:\/\/semmelweis.hu\/bioinformatika\/oktatas\/bioinformatika-targy\/a-targy-reszletes-tematikaja\/","title":{"rendered":"A t\u00e1rgy r\u00e9szletes tematik\u00e1ja"},"content":{"rendered":"
\n

A t\u00e1rgy r\u00e9szletes tematik\u00e1ja:<\/strong><\/p>\n

I. blokk: Bioinformatikai alapok<\/b><\/p>\n

A1 – Bevezet\u00e9s a bioinformatika tant\u00e1rgyba (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nA2 – Tanul\u00f3 \u00e9s teszthalmaz alkalmaz\u00e1sa (Dr. Fekete J\u00e1nos Tibor)
\nA3 – Statisztikai hib\u00e1k \u00e9s a dichotom\u00e1nia fogalma (Dr. Fekete J\u00e1nos Tibor)
\nA4 – T\u00fal\u00e9l\u00e9s-elemz\u00e9s: Cox regresszi\u00f3 \u00e9s a Kaplan-Meier plot* (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nA5 – ROC elemz\u00e9s: szenzitivit\u00e1s, specificit\u00e1s, el\u0151rejelz\u00e9s \u00e9rt\u00e9ke* (Dr. Fekete J\u00e1nos Tibor)
\nA6 – V\u00e1ltoz\u00f3k k\u00f6z\u00f6tti korrel\u00e1ci\u00f3 (Dr. Menyh\u00e1rt Ot\u00edlia)
\nA7 – T\u00f6bbsz\u00f6r\u00f6s hipot\u00e9zis teszt* (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nA8 – K\u00e9t csoport grafikai \u00f6sszehasonl\u00edt\u00e1sa: boxplot, heged\u0171-plot, s\u0171r\u0171s\u00e9g-plot, h\u0151t\u00e9rk\u00e9p, korrel\u00e1ci\u00f3, m\u00e1trix (Dr. Bartha \u00c1ron)
\nA9 – Regresszi\u00f3 alapok* (Dr. Menyh\u00e1rt Ot\u00edlia)
\nA10 – Regresszi\u00f3 m\u00f3dszerek (Dr. Menyh\u00e1rt Ot\u00edlia)<\/p>\n

II. blokk: Genomika \u00e9s transzkriptomika<\/b><\/p>\n<\/div>\n

\n


\n<\/b>G1 – szekven\u00e1l\u00e1s bevezet\u0151 (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nG2 – Hasonl\u00f3 g\u00e9nek \u00e9s feh\u00e9rj\u00e9k, blast (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nG3 – Az adatok min\u0151s\u00e9gellen\u0151rz\u00e9se * (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG4 – Adatok illeszt\u00e9se referencia genomra* (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG5 – Mut\u00e1ci\u00f3k azonos\u00edt\u00e1sa (SNV, indelek) norm\u00e1l \u00e9s tumor mint\u00e1b\u00f3l* (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG6 – Mut\u00e1ci\u00f3k k\u00f6vetkezm\u00e9ny\u00e9nek meghat\u00e1roz\u00e1sa* (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG7 – Klinikailag relev\u00e1ns vari\u00e1nsokra sz\u0171r\u00e9s, ClinVar, dbSNP* (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG8 – CNV (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nG9 – Mintafeldolgoz\u00e1si mell\u00e9kterm\u00e9kek azonos\u00edt\u00e1sa \u00e9s kisz\u0171r\u00e9se (Dr. Menyh\u00e1rt Ot\u00edlia)
\nG10 – Transzkriptomika: RNAseq adatok feldolgoz\u00e1sa (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nG11 – Single cell adatok feldolgoz\u00e1sa (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)<\/p>\n

III. blokk: Proteomika<\/b><\/p>\n

P1 – Proteomika \u00e9s transzkriptomika: pre-processing: (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nP2 – Antitest alap\u00fa ki\u00e9rt\u00e9kel\u00e9s (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nP3 – A t\u00f6megspektrometria ki\u00e9rt\u00e9kel\u00e9se (Dr. Bartha \u00c1ron)
\nP4 – A t\u00f6megspektometria el\u0151nyei \u00e9s h\u00e1tr\u00e1nyai (Dr. Bartha \u00c1ron)
\nP5 – Feh\u00e9rjeszerkezet meghat\u00e1roz\u00e1s: fizikai\/k\u00e9miai m\u00f3dszerek (Dr. Tusn\u00e1dy G\u00e1bor)
\nP6 – Feh\u00e9rjeszerkezet modellez\u00e9s: az egyszer\u0171t\u0151l a bonyolultig (Dr. Tusn\u00e1dy G\u00e1bor)
\nP7 – Transzmembr\u00e1n feh\u00e9rj\u00e9k szerkezet\u00e9nek modellez\u00e9se (Dr. Tusn\u00e1dy G\u00e1bor)
\nP8 – Biol\u00f3giai funkci\u00f3k meghat\u00e1roz\u00e1sa \u00e9s az Uniprot (Dr. Munk\u00e1csy Gy\u00f6ngyi)<\/p>\n

IV. blokk: Mesters\u00e9ges intelligencia<\/b><\/p>\n

M1 – G\u00e9pi tanul\u00e1s eszk\u00f6zei (Dr. Fekete J\u00e1nos Tibor)
\nM2 – A Bayes szab\u00e1ly (Dr. Fekete J\u00e1nos Tibor)
\nM3 – F\u0151komponens elemz\u00e9s (Dr. Bartha \u00c1ron)
\nM4 – F\u00fcrtelemz\u0151 programok: t\u00e1vols\u00e1gm\u00e9r\u00e9s (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nM5 – F\u00fcrtelemz\u0151 programok: clustering* (Prof.Dr. Gy\u0151rffy Bal\u00e1zs)
\nM6 – Rekurz\u00edv neur\u00e1lis h\u00e1l\u00f3 (Dr. Menyh\u00e1rt Ot\u00edlia)
\nM7 – Konvol\u00faci\u00f3s neur\u00e1lis h\u00e1l\u00f3 (Dr. Menyh\u00e1rt Ot\u00edlia)
\nM8 – D\u00f6nt\u00e9si fa a klinikai gyakorlatban (Dr. Bartha \u00c1ron)
\nM9 – V\u00e1ltoz\u00f3 szelekci\u00f3* (Dr. Menyh\u00e1rt Ot\u00edlia)
\nM10 – Support Vector Machines* (Dr. Fekete J\u00e1nos Tibor)
\nM11 – Az \u00e1ltal\u00e1nos el\u0151tanult transzform\u00e1tor (Dr. Fekete J\u00e1nos Tibor)<\/p>\n

V. blokk: Integrat\u00edv tudom\u00e1ny<\/b><\/p>\n

X1 – Multi-omika (Dr. Menyh\u00e1rt Ot\u00edlia)
\nX2 – G\u00e9nontol\u00f3gia (Dr. Fekete J\u00e1nos Tibor)
\nX3 – A reproduk\u00e1lhat\u00f3s\u00e1g k\u00e9rd\u00e9se orvosbiol\u00f3giai kutat\u00e1sokban (Dr. Menyh\u00e1rt Ot\u00edlia)
\nX4 – Chip-seq \u00e9s ATAC-seq adatok ki\u00e9rt\u00e9kel\u00e9se (Dr. B\u00e1lint B\u00e1lint)
\nX5 – DNS metil\u00e1ci\u00f3s adatok ki\u00e9rt\u00e9kel\u00e9se (Dr. B\u00e1lint B\u00e1lint)
\nX6 – Epigenetikai adatb\u00e1zisok (Dr. B\u00e1lint B\u00e1lint)
\nX7 – Az Excel haszn\u00e1lata adatb\u00e1ziskezel\u00e9shez (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nX8 \u2013 Irodalomkutat\u00e1s \u00e9s AI (Dr. Munk\u00e1csy Gy\u00f6ngyi)
\nX9 – A REDcap felhaszn\u00e1l\u00e1sa (Dr. Menyh\u00e1rt Ot\u00edlia)
\nX10 – Id\u0151torz\u00edt\u00e1s \u00e9s f\u00fcgg\u0151s\u00e9g (Dr. Menyh\u00e1rt Ot\u00edlia)
\nX11 – Blokkl\u00e1nc \u00e9s adatbiztons\u00e1g (Dr. Bartha \u00c1ron)<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

A t\u00e1rgy r\u00e9szletes tematik\u00e1ja: I. blokk: Bioinformatikai alapok A1 – Bevezet\u00e9s a bioinformatika tant\u00e1rgyba (Prof.Dr. Gy\u0151rffy Bal\u00e1zs) A2 – Tanul\u00f3 \u00e9s teszthalmaz alkalmaz\u00e1sa (Dr. Fekete J\u00e1nos Tibor) A3 – Statisztikai hib\u00e1k \u00e9s a dichotom\u00e1nia fogalma (Dr. Fekete J\u00e1nos Tibor) A4 – T\u00fal\u00e9l\u00e9s-elemz\u00e9s: Cox regresszi\u00f3 \u00e9s a Kaplan-Meier plot* (Prof.Dr. Gy\u0151rffy Bal\u00e1zs) A5 – ROC elemz\u00e9s: …<\/p>\n","protected":false},"author":101687,"featured_media":0,"parent":6522,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-6815","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/6815","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/users\/101687"}],"replies":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/comments?post=6815"}],"version-history":[{"count":14,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/6815\/revisions"}],"predecessor-version":[{"id":10685,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/6815\/revisions\/10685"}],"up":[{"embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/pages\/6522"}],"wp:attachment":[{"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/media?parent=6815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/categories?post=6815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/semmelweis.hu\/bioinformatika\/wp-json\/wp\/v2\/tags?post=6815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}