Using Machine Learning for Drug Discovery in IBD

Sarah Kidwai; David Rojas-Velazquez; Dora Kožinec; Lukas Vaišvilas; Paula Perez-Pardo; Alejandro Lopez-Rincon

Using Machine Learning for Drug Discovery in IBD

Sarah Kidwai
, David Rojas-Velazquez
, Dora Kožinec
, Lukas Vaišvilas
, Paula Perez-Pardo
, Alejandro Lopez-Rincon

Julius Center, dept. Data Science & Biostatistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Inflammatory Bowel Disease (IBD) refers to two conditions: ulcerative colitis (UC) and Crohn disease (CD) [1]. IBD has a range of clinical symptoms including abdominal pain, diarrhoea, weight loss, constipation, and fatigue [2]. The main cause of IBD is unknown, but it is the result of a weakened immune system. The possible causes of IBD involve genetic and environmental factors. Unfortunately, with conventional diagnostic methods it is hard to predict therapeutic responsiveness of patients beforehand [3]. Genetic expression may be a valuable prediction tool to determine patient’ responsiveness to treatment.

Original language	English
Title of host publication	CMBBE 2023 - 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2023)
Place of Publication	Paris, France
Publisher	HAL open science
Pages	1-2
Number of pages	2
Publication status	Published - 1 May 2023

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https://hal.science/hal-04097613

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title = "Using Machine Learning for Drug Discovery in IBD",

abstract = "Inflammatory Bowel Disease (IBD) refers to two conditions: ulcerative colitis (UC) and Crohn disease (CD) [1]. IBD has a range of clinical symptoms including abdominal pain, diarrhoea, weight loss, constipation, and fatigue [2]. The main cause of IBD is unknown, but it is the result of a weakened immune system. The possible causes of IBD involve genetic and environmental factors. Unfortunately, with conventional diagnostic methods it is hard to predict therapeutic responsiveness of patients beforehand [3]. Genetic expression may be a valuable prediction tool to determine patient{\textquoteright} responsiveness to treatment.",

author = "Sarah Kidwai and David Rojas-Velazquez and Dora Ko{\v z}inec and Lukas Vai{\v s}vilas and Paula Perez-Pardo and Alejandro Lopez-Rincon",

year = "2023",

month = may,

day = "1",

language = "English",

pages = "1--2",

booktitle = "CMBBE 2023 - 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2023)",

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Using Machine Learning for Drug Discovery in IBD. / Kidwai, Sarah; Rojas-Velazquez, David; Kožinec, Dora et al.
CMBBE 2023 - 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2023). Paris, France: HAL open science, 2023. p. 1-2.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Using Machine Learning for Drug Discovery in IBD

AU - Kidwai, Sarah

AU - Rojas-Velazquez, David

AU - Kožinec, Dora

AU - Vaišvilas, Lukas

AU - Perez-Pardo, Paula

AU - Lopez-Rincon, Alejandro

PY - 2023/5/1

Y1 - 2023/5/1

N2 - Inflammatory Bowel Disease (IBD) refers to two conditions: ulcerative colitis (UC) and Crohn disease (CD) [1]. IBD has a range of clinical symptoms including abdominal pain, diarrhoea, weight loss, constipation, and fatigue [2]. The main cause of IBD is unknown, but it is the result of a weakened immune system. The possible causes of IBD involve genetic and environmental factors. Unfortunately, with conventional diagnostic methods it is hard to predict therapeutic responsiveness of patients beforehand [3]. Genetic expression may be a valuable prediction tool to determine patient’ responsiveness to treatment.

AB - Inflammatory Bowel Disease (IBD) refers to two conditions: ulcerative colitis (UC) and Crohn disease (CD) [1]. IBD has a range of clinical symptoms including abdominal pain, diarrhoea, weight loss, constipation, and fatigue [2]. The main cause of IBD is unknown, but it is the result of a weakened immune system. The possible causes of IBD involve genetic and environmental factors. Unfortunately, with conventional diagnostic methods it is hard to predict therapeutic responsiveness of patients beforehand [3]. Genetic expression may be a valuable prediction tool to determine patient’ responsiveness to treatment.

M3 - Conference contribution

SP - 1

EP - 2

BT - CMBBE 2023 - 18th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2023)

PB - HAL open science

CY - Paris, France

ER -

Using Machine Learning for Drug Discovery in IBD

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