Kochan, Necla

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14365/7548
Name Variants
Koçhan, Necla
Koçhan, N.
Kayaalp, Necla
Job Title
Email Address
necla.kayaalp@gmail.com
necla.kochan@ieu.edu.tr
Main Affiliation
02.02. Mathematics
Status
Current Staff
Website
ORCID ID
0000-0003-2355-4826
Scopus Author ID
57222006264
Turkish CoHE Profile ID
AECF80143C53BF59
Google Scholar ID
eGWINX8AAAAJ
WoS Researcher ID
JXM-5057-2024
Files
Necla_Kochan.png (98.11 KB)

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

0

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

0

Research Products

15

LIFE ON LAND
LIFE ON LAND Logo

0

Research Products

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

0

Research Products

1

NO POVERTY
NO POVERTY Logo

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

0

Research Products

10

REDUCED INEQUALITIES
REDUCED INEQUALITIES Logo

0

Research Products

14

LIFE BELOW WATER
LIFE BELOW WATER Logo

0

Research Products

2

ZERO HUNGER
ZERO HUNGER Logo

0

Research Products

13

CLIMATE ACTION
CLIMATE ACTION Logo

0

Research Products

5

GENDER EQUALITY
GENDER EQUALITY Logo

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

0

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

1

Research Products
Documents

16

Citations

100

h-index

6

Go to Scopus profile
Documents

14

Citations

77

Go to WoS profile
Scholarly Output

13

Articles

10

Views / Downloads

36/94

Supervised MSc Theses

1

Supervised PhD Theses

1

WoS Citation Count

26

Scopus Citation Count

39

WoS h-index

3

Scopus h-index

4

Patents

0

Projects

0

WoS Citations per Publication

2.00

Scopus Citations per Publication

3.00

Open Access Source

11

Supervised Theses

2

Google Analytics Visitor Traffic

JournalCount
Turkish Journal of Biology2
Cumhuriyet Science Journal1
European Journal of Immunology1
Expert Systems Wıth Applıcatıons1
Internatıonal Journal of Intellıgent Systems1
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Scholarly Output Search Results

Now showing 1 - 10 of 13
  • Thumbnail Image
    Article
    Citation - WoS: 5
    Citation - Scopus: 4
    A Fuzzy Bayesian Classifier With Learned Mahalanobis Distance
    (Wiley, 2014) Kayaalp, Necla; Arslan, Guvenc
    Recent developments show that naive Bayesian classifier (NBC) performs significantly better in applications, although it is based on the assumption that all attributes are independent of each other. However, in the NBC each variable has a finite number of values, which means that in large data sets NBC may not be so effective in classifications. For example, variables may take continuous values. To overcome this issue, many researchers used fuzzy naive Bayesian classification for partitioning the continuous values. On the other hand, the choice of the distance function is an important subject that should be taken into consideration in fuzzy partitioning or clustering. In this study, a new fuzzy Bayes classifier is proposed for numerical attributes without the independency assumption. To get high accuracy in classification, membership functions are constructed by using the fuzzy C-means clustering (FCM). The main objective of using FCM is to obtain membership functions directly from the data set instead of consulting to an expert. The proposed method is demonstrated on the basis of two well-known data sets from the literature, which consist of numerical attributes only. The results show that the proposed the fuzzy Bayes classification is at least comparable to other methods.
  • Thumbnail Image
    Article
    A Bibliometric and Visual Analysis of Publications on Low-Density Lipoprotein Cholesterol Estimating Equations
    (2024) Bolat, Serkan; Yerlitas, Serra Ilayda; Cephe, Ahu; Kochan, Necla; Zararsız, Gözde Ertürk; Doğan, Halef Okan; Zararsiz, Gokmen
    The concentration of low-density lipoprotein cholesterol (LDL-C) is recognized as a crucial factor in cardiovascular health. This study aims to conduct a comprehensive bibliometric and visual analysis to provide a comprehensive review of current research trends and patterns in this research area. We retrieved the publications from the Web of Science (WoS) database and conducted the bibliometric analyses using VOSviewer software, bibliometrix R package, and biblioshiny web tool. The analysis was conducted on 620 original articles and review papers published between 1990 and 2023 from institutions located in 62 different countries and published in 329 journals. The countries with the most articles were the US, China, and Japan. The most productive journals were Clinica Chemica Acta, Clinical Biochemistry, and Clinical Chemistry; while the most cited journals were Clinical Chemistry, Circulation, and JAMA-Journal of the American Medical Association. The co-occurrence network visualizations of keywords and terms provided a global overview of LDL-C estimating equations. The study presented provides valuable insights into potential research avenues within the examined field, serving as a foundation for future research initiatives.
  • Thumbnail Image
    Article
    Citation - WoS: 6
    Citation - Scopus: 10
    A New Local Covariance Matrix Estimation for the Classification of Gene Expression Profiles in High Dimensional Rna-Seq Data
    (Pergamon-Elsevier Science Ltd, 2021) Kochan, Necla; Tütüncü, Gözde Yazgı; Giner, Goknur
    Recent developments in the next-generation sequencing based on RNA-sequencing (RNA-Seq) allow researchers to measure the expression levels of thousands of genes for multiple samples simultaneously. In order to analyze these kinds of data sets, many classification models have been proposed in the literature. Most of the existing classifiers assume that genes are independent; however, this is not a realistic approach for real RNA-Seq classification problems. For this reason, some other classification methods, which incorporates the dependence structure between genes into a model, are proposed. Quantile transformed Quadratic Discriminant Analysis (qtQDA) proposed recently is one of those classifiers, which estimates covariance matrix by Maximum Likelihood Estimator. However, MLE may not reflect the real dependence between genes. For this reason, we propose a new approach based on local dependence function to estimate the covariance matrix to be used in the qtQDA classification model. This new approach assumes the dependencies between genes are locally defined rather than complete dependency. The performances of qtQDA classifier based on two different covariance matrix estimates are compared over two real RNA-Seq data sets, in terms of classification error rates. The results show that using local dependence function approach yields a better estimate of covariance matrix and increases the performance of qtQDA classifier.
  • Thumbnail Image
    Article
    Citation - Scopus: 7
    Qtqda: Quantile Transformed Quadratic Discriminant Analysis for High-Dimensional Rna-Seq Data
    (PeerJ Inc., 2019) Koçhan, N.; Tütüncü, Gözde Yazgı; Smyth, G.K.; Gandolfo, L.C.; Giner, G.
    Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian quadratic discriminant analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available on https://github.com/goknurginer/qtQDA. Copyright 2019 Koçhan et al.
  • Thumbnail Image
    Doctoral Thesis
    A New Rna-Seq Data Classifier Based on Quantile Transformation
    (İzmir Ekonomi Üniversitesi, 2020) Koçhan, Necla; Aşçı, Gözde Yazgı Tütüncü
    Son zamanlarda kanser araştırmalarında, bilinen bir kanser tipi olan bir hastanın o kanserin çeşidine göre doğru sınıflandırılması o hasta için daha iyi tahminlere dayanan ve kişiye özel tedavi sağlamaktadır. Bu nedenle, hastanın kanser çeşidine göre sınıflandırılması çok önemlidir ve bu, genetik bilgi kullanılarak yapılabilinmektedir. Mevcut sınıflandırıcıların çoğu genlerin bağımsız olduğu varsayımına dayanmaktadır; ancak, bu varsayım asıl RNA-Sekans sınıflandırma problemleri için gerçekçi bir yaklaşım değildir. Bu nedenle, bu tezde, genler arasındaki bağımlılık yapısını dikkate alan yeni bir sınıflandırıcı önerilmektedir. Genler arasındaki bağımlılık önce kovaryans matrisi ve daha sonra lokal kovaryans matrisi ile modellenmektedir. Lokal kovaryans matrisi, lokal bağımlılık fonksiyonu kullanılarak tahmin edilmektedir. Sınıflama algoritması R programlama dilinde kodlanmış olup RNA-Sekans verileri için yeni bir sınıflama paketi geliştirilmiştir. Yeni sınıflandırıcının performansı, gerçek RNA-Sekans verileri kullanılarak mevcut sınıflandırıcılar ile sınıflandırma hataları açısından karşılaştırılmıştır.
  • Thumbnail Image
    Conference Object
  • Thumbnail Image
    Master Thesis
    Fuzzy Bayes classification
    (İzmir Ekonomi Üniversitesi, 2013) Kayaalp, Necla; Arslan, Güvenç; Tütüncü, Gözde Yazgı
    Bu tezde, bağımsızlık varsayımı dikkate alınmadan sayısal niteleyiciler için yeni bir Bulanık Bayes Sınıflaması önerilmiştir. Sınıflamada, yüksek doğruluğu elde etmek için, Bulanık C-Means Kümelemesi (BCM) kullanılarak üyelik fonksiyonları oluşturulmuştur. BCM kullanımındaki temel amaç, bir uzmana danışmak yerine üyelik fonksiyonlarını doğrudan veri setinden elde etmektir. Önerilen yöntem, yalnızca sayısal niteleyicileri içeren ve alanyazında iyi bilinen iki veri seti üzerinde gösterilmistir.
  • Thumbnail Image
    Article
    Classification of Colon Cancer Patients Into Consensus Molecular Subtypes Using Support Vector Machines
    (2023) Koçhan, Necla; Dayanç, Barış Emre
    Background/aim: The molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the consensus molecular subtypes (CMS), developed by the Colorectal Cancer Subtyping Consortium. CMS-specific RNA-Seq-dependent classification approaches are recent, with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using their RNA-seq profiles. Materials and methods: We first identified subtype-specific and survival-associated genes using the Fuzzy C-Means algorithm and log- rank test. We then classified patients using support vector machines with backward elimination methodology. Results: We optimized RNA-seq-based classification using 25 genes with a minimum classification error rate. In this study, we reported the classification performance using precision, sensitivity, specificity, false discovery rate, and balanced accuracy metrics. Conclusion: We present a gene list for colon cancer classification with minimum classification error rates and observed the lowest sensitivity but the highest specificity with CMS3-associated genes, which significantly differed due to the low number of patients in the clinic for this group.
  • Thumbnail Image
    Article
    A Copula-Based Classification Using Agglomerated Feature Selection_Extraction: An Application in Cervical Cancer Diagnostic
    (Ankara University, Faculty of Science, 2025) Kochan, Necla; Sheikhi, Ayyub
    The use of gene-expression datasets has significantly enhanced our understanding of complex diseases such as cancer. The importance of the relationship between genes in analyzing such datasets has been highlighted, indicating their crucial role in diagnosing the disease accurately. In this study, we investigate the associated copulas between attributes to extract fundamental block-related components. Subsequently, we perform a classification algorithm based on these components to classify a labeled target variable. Specifically, examining the practical implications and effectiveness of our approach in real-world scenarios, we provide a novel illustrative application in cervical cancer classification.
  • Thumbnail Image
    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Classification of Colon Cancer Patients Into Consensus Molecular Subtypes Using Support Vector Machines
    (TUBITAK, 2023) Kochan, Necla; Dayanç, Barış Emre
    Background/aim: The molecular heterogeneity of colon cancer has made classification of tumors a requirement for effective treatment. One of the approaches for molecular subtyping of colon cancer patients is the consensus molecular subtypes (CMS), developed by the Colorectal Cancer Subtyping Consortium. CMS-specific RNA-Seq-dependent classification approaches are recent, with relatively low sensitivity and specificity. In this study, we aimed to classify patients into CMS groups using their RNA-seq profiles. Materials and methods: We first identified subtype-specific and survival-associated genes using the Fuzzy C-Means algorithm and log-rank test. We then classified patients using support vector machines with backward elimination methodology. Results: We optimized RNA-seq-based classification using 25 genes with a minimum classification error rate. In this study, we reported the classification performance using precision, sensitivity, specificity, false discovery rate, and balanced accuracy metrics. Conclusion: We present a gene list for colon cancer classification with minimum classification error rates and observed the lowest sensitivity but the highest specificity with CMS3-associated genes, which significantly differed due to the low number of patients in the clinic for this group. © TÜBİTAK.