TY - GEN
T1 - Predicting HIV-1 Protease and Reverse Transcriptase Drug Resistance Using Fuzzy Cognitive Maps
AU - Grau, Isel
AU - Nápoles, Gonzalo
AU - García Lorenzo, María M.
PY - 2013
Y1 - 2013
N2 - Several antiviral drugs have been approved for treating HIV infected patients. These drugs inhibit the function of proteins which are essential in the virus life cycle, thus preventing the virus reproduction. However, due to its high mutation rate the HIV is capable to develop resistance to administered therapy. For this reason, it is important to study the resistance mechanisms of the HIV proteins in order to make a better use of existing drugs and design new ones. In the last ten years, numerous statistical and machine learning approaches were applied for predicting drug resistance from protein genome information. In this paper we first review the most relevant techniques reported for addressing this problem. Afterward, we describe a Fuzzy Cognitive Map based modeling which allows representing the causal interactions among the protein positions and their influence on the resistance. Finally, an extended comparison experimentation is carried out, which reveals that this model is competitive with well-known approaches and notably outperforms other techniques from literature.
AB - Several antiviral drugs have been approved for treating HIV infected patients. These drugs inhibit the function of proteins which are essential in the virus life cycle, thus preventing the virus reproduction. However, due to its high mutation rate the HIV is capable to develop resistance to administered therapy. For this reason, it is important to study the resistance mechanisms of the HIV proteins in order to make a better use of existing drugs and design new ones. In the last ten years, numerous statistical and machine learning approaches were applied for predicting drug resistance from protein genome information. In this paper we first review the most relevant techniques reported for addressing this problem. Afterward, we describe a Fuzzy Cognitive Map based modeling which allows representing the causal interactions among the protein positions and their influence on the resistance. Finally, an extended comparison experimentation is carried out, which reveals that this model is competitive with well-known approaches and notably outperforms other techniques from literature.
U2 - 10.1007/978-3-642-41827-3_24
DO - 10.1007/978-3-642-41827-3_24
M3 - Conference contribution
SN - 978-3-642-41826-6
T3 - Lecture Notes in Computer Science
SP - 190
EP - 197
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A2 - Ruiz-Shulcloper, José
A2 - Sanniti di Baja, Gabriella
PB - Springer
ER -
