Výsledky vyhledávání - "Consolata Gakii"

Akademický článek

Feature selection for classification using WGCNA and Spread Sub-Sample for an imbalanced rheumatoid arthritis RNASEQ data

Autor: Consolata Gakii, Victoria Mukami, Boaz Too

Publikováno v: Informatics in Medicine Unlocked, Vol 43, Iss , Pp 101402- (2023)

An imbalanced classification problem occurs when the distribution of samples among different classes is uneven or biased. Handling small and imbalanced training datasets poses a notable challenge in machine learning, especially in domains such as bio

Externí odkaz: https://doaj.org/article/5354d9a439d64d2a94bbdeec09cd40e8

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Akademický článek

Maize Leaf Disease Detection using Convolutional Neural Networks

Autor: MICHENI Maurice Murimi, Margaret KINYUA, Boaz TOO, Consolata GAKII

Publikováno v: Journal of Applied Computer Science & Mathematics, Vol 15, Iss 1, Pp 15-20 (2021)

Digital image analysis has brought great benefit to agriculture. Plant diseases are an important impediment to food security because they lead to reduction in the quality and quantity of agricultural produce. Detection of plant diseases relies on the

Externí odkaz: https://doaj.org/article/f0f036bf45db43b489adb76601c961fa

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Akademický článek

Annotations of novel antennae-expressed genes in male Glossina morsitans morsitans tsetse flies.

Autor: Billiah K Bwana, Paul O Mireji, George F Obiero, Consolata Gakii, Modesta O Akoth, Julius N Mugweru, Franklin N Nyabuga, Benson M Wachira, Rosemary Bateta, Margaret M Ng'ang'a, Ahmed Hassanali

Publikováno v: PLoS ONE, Vol 17, Iss 8, p e0273543 (2022)

Tsetse flies use antennal expressed genes to navigate their environment. While most canonical genes associated with chemoreception are annotated, potential gaps with important antennal genes are uncharacterized in Glossina morsitans morsitans. We gen

Externí odkaz: https://doaj.org/article/103e573fc74b411eb0d987ed746c96a5

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Akademický článek

In silico-driven analysis of the Glossina morsitans morsitans antennae transcriptome in response to repellent or attractant compounds

Autor: Consolata Gakii, Billiah Kemunto Bwana, Grace Gathoni Mugambi, Esther Mukoya, Paul O. Mireji, Richard Rimiru

Publikováno v: PeerJ, Vol 9, p e11691 (2021)

Background High-throughput sequencing generates large volumes of biological data that must be interpreted to make meaningful inference on the biological function. Problems arise due to the large number of characteristics p (dimensions) that describe

Externí odkaz: https://doaj.org/article/6791252c3b1943cbaf098f8a2e0189c5

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Akademický článek

Identification of cancer related genes using feature selection and association rule mining

Autor: Consolata Gakii, Richard Rimiru

Publikováno v: Informatics in Medicine Unlocked, Vol 24, Iss , Pp 100595- (2021)

High throughput sequencing generates large volumes of high dimensional data. Identifying informative features from the generated big data is always a challenge. Feature selection reduces complex data into a smaller number of variables while preservin

Externí odkaz: https://doaj.org/article/70b768d6a9d149fbbdad26ce846c1f34

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Akademický článek

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Autor: Consolata Gakii, Paul O. Mireji, Richard Rimiru

Publikováno v: Algorithms, Vol 15, Iss 1, p 21 (2022)

Analysis of high-dimensional data, with more features (p) than observations (N) (p>N), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a grap

Externí odkaz: https://doaj.org/article/2b6b6001a0114ea78488cfa6fc099a32

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