A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL)

2018 ◽  
Vol 59 (1) ◽  
pp. 586-596 ◽  
Author(s):  
Domenico Alberga ◽  
Daniela Trisciuzzi ◽  
Michele Montaruli ◽  
Francesco Leonetti ◽  
Giuseppe Felice Mangiatordi ◽  
...  
Keyword(s):  
Similarity Search ◽  
Drug Target ◽  
Search Algorithm ◽  
New Approach ◽  
Bioactivity Prediction

Distributed similarity search algorithm in distributed heterogeneous multimedia databases

2000 ◽  
Vol 75 (1-2) ◽  
pp. 35-42 ◽  
Author(s):  
Ju-Hong Lee ◽  
Deok-Hwan Kim ◽  
Seok-Lyong Lee ◽  
Chin-Wan Chung ◽  
Guang-Ho Cha
Keyword(s):  
Similarity Search ◽  
Search Algorithm ◽  
Multimedia Databases

scholarly journals Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition

2020 ◽  
Vol 6 (11) ◽  
pp. 112
Author(s):  
Faisal R. Al-Osaimi
Keyword(s):  
Search Algorithm ◽  
Recognition Performance ◽  
Feature Space ◽  
Training Data ◽  
Equivalence Relations ◽  
Graph Search ◽  
Search Problem ◽  
New Approach ◽  
Unique Approach ◽  
Training Examples

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

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