Table 1 Summary of existing ML-based models for thermophilic protein prediction.

Author (year)	Classifier ^a	Features ^b	Evaluation strategy^c	Web server availability^d
Zhang et al.³¹	PLS	AAC	5CV/IND	No
Zhang et al.³²	LogitBoost	AAC	5CV/IND	No
Gromiha et al.²⁷	NN	AAC	5CV/IND	No
Montanucci et al.²¹	SVM	AAC, DPC	5CV	Not accessible
Lin et al.²⁰	SVM	AAC, GGAC	Jackknife	Yes
Wang et al.²⁴	SVM	AAC, DPC, PCP, CTD	5CV	No
Nakariyakul et al.²⁸	SVM	AAC, DPC	5CV/IND	No
Zuo et al.³³	KNN	AAC	Jackknife	Not accessible
Wang et al.³⁰	SVM	AAC, GGAC	5CV/IND	No
Fan et al.²⁵	SVM	AAC, pka, PSSM	10CV/IND	No
Tang et al.²⁹	SVM	k-mer	5CV	No
Feng et al.²⁶	SVM	ACC, DPC, PCP,RAAC	10CV/IND	No
Charoenkwan et al. (this study)	SCM	DPS	10CV/IND	Yes

^aKNN k-nearest neighbor, NN neural networks, PLS partial least-square regression, SVM support vector machine.
^bAAC amino acid composition, CTD composition-transition-distribution, DPC dipeptide composition, DPS dipeptide propensity scores, GGAP g-gap dipeptide composition, k-mer fragment-based technique, pka acid dissociation constant, PCP physicochemical properties, PseACC pseudo amino acid composition, PSSM position specific scoring matrix, RACC reduce amino acid composition, TC tripeptide composition.
^c5CV fivefold cross-validation, 10CV tenfold cross-validation, jackknif jackknife cross-validation, IND independent test.
^dNot accessible: the webserver was not functional during the preparation of this manuscript.

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