Taşçı, Aslı

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Profile URL
https://hdl.handle.net/20.500.14365/7358
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Email Address
asli.tasci@ieu.edu.tr
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Former Staff
Website
ORCID ID
0000-0003-2091-5200
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Turkish CoHE Profile ID
8124C6CE6E59E407
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WoS Researcher ID

Sustainable Development Goals

5

GENDER EQUALITY
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

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13

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DECENT WORK AND ECONOMIC GROWTH
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14

LIFE BELOW WATER
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17

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2

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4

QUALITY EDUCATION
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SUSTAINABLE CITIES AND COMMUNITIES
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PEACE, JUSTICE AND STRONG INSTITUTIONS
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GOOD HEALTH AND WELL-BEING
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RESPONSIBLE CONSUMPTION AND PRODUCTION
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REDUCED INEQUALITIES
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Scholarly Output

2

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Supervised MSc Theses

1

Supervised PhD Theses

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WoS Citation Count

2

Scopus Citation Count

4

WoS h-index

1

Scopus h-index

1

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0

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WoS Citations per Publication

1.00

Scopus Citations per Publication

2.00

Open Access Source

1

Supervised Theses

1

JournalCount
2017 10Th Internatıonal Conference on Electrıcal And Electronıcs Engıneerıng (Eleco)1
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  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 4
    A Comparison of Feature Selection Algorithms for Cancer Classification Through Gene Expression Data: Leukemia Case
    (IEEE, 2017) Taşçı, Aslı; İnce, Türker; Guzelis, Cuneyt
    In this study, three different feature selection algorithms are compared using Support Vector Machines as classifier for cancer classification through gene expression data. The ability of feature selection algorithms to select an optimal gene subset for a cancer type is evaluated by the classification ability of selected genes. A publicly available micro array dataset is employed for gene expression values. Selected gene subsets were able to classify subtypes of the considered cancer type with high accuracies and showed that these feature selection methods were applicable for bio-marker gene selection.
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    Master Thesis
    A Comparative Evaluation of Feature Selection Algorithms for Cancer Classification Through Gene Expression Data
    (İzmir Ekonomi Üniversitesi, 2016) Taşçı, Aslı; İnce, Türker; Güzeliş, Cüneyt
    Kanser teşhisi konan insanların sayısı her geçen gün artmaktadır. Doktorlar kanser türlerini, görüntüleme teknolojileri, kan analizi ve doku biyopsilerinden elde edilen sonuçları yorumlayarak teşhis ederler. Kanser hücrede başlar. Bu nedenle, kanser hücresinin genetik yapısının incelenmesi, uzun vadede daha güvenilir ve bilgilendiricidir. Ayrıca, bu hücrelerin genetik yapısının analizi, hedef ilaç tedavilerinde kullanılabilen belirteç genleri tanımlarken ve gen ağlarını, genler ile gen ürünleri arasındaki ilişkileri ve genlerin belirli hücre sinyal yolakları üzerindeki etkilerini anlamakta da yardımcı olabilir. Mikro-dizilinler bu alandaki veri kaynaklarından biridir. Gen ifade değerlerini belirlerler ve kanseri teşhis etmek veya kanser türlerini sınıflandırmak için kullanılabilirler. Bu tezde önerilen yöntemde, gen ifadesi verileri, uygun bir gen alt kümesi bulmak ve kanser türlerini sınıflandırmak için istatistiksel teknikler ve makine öğrenme teknikleri kullanılarak analiz edilir. İstatistiksel filtre yaklaşımları, anlamlı bir gen alt kümesi elde etmek için öznitelik seçme yöntemleri olarak kullanılır. Destek vektör makineleri ve çok katmanlı algılayıcı da öznitelik seçme algoritmalarını test etmek ve kanser türlerini sınıflandırmak için kullanılır.